-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Columbia, US-SC
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:43
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Philadelphia, US-PA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:39
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Portland, US-OR
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:35
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Tulsa, US-OK
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:32
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Oklahoma City, US-OK
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:31
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Eugene, US-OR
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:27
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Wilmington, US-NC
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:25
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Cincinnati, US-OH
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:20
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Fargo, US-ND
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:16
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Cleveland, US-OH
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:13
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Columbus, US-OH
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:10
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Minot, US-ND
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:04
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Albany, US-NY
Salary / Rate: Not Specified
Posted: 2026-10-07 10:42:00
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Las Cruces, US-NM
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:56
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Charlotte, US-NC
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:53
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Rochester, US-NY
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:47
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Buffalo, US-NY
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:44
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Albuquerque, US-NM
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:39
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Newark, US-NJ
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:34
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Lebanon, US-NH
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:30
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Reno, US-NV
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:25
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Cherry Hill, US-NJ
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:21
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Manchester, US-NH
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:16
-
Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
....Read more...
Type: Permanent Location: Missoula, US-MT
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:13
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Essential Duties and Responsibilities:
- Own Tier 3 (Data Management) and Tier 4 (Solution Foundation): Lead ontology development and extension (including AIModel and Identity entities), metadata standards, data-quality management (target>99%), integration pipelines, and lifecycle policies.
- Lead the design and implementation of the enterprise data architecture, including the Azure Data Lakehouse, Purview governance layer, Synapse analytics environment, Data Factory pipelines, and machine-learning workspaces.
- Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and DataGuard prototypes (RBAC/ABAC, OpenTDF).
- Build and scale AI-readiness capabilities, including RAG-ready datasets, vector stores, ethical model governance, bias checks, and AIModel tracking.
- Lead technical pilots and segment rollouts (e.g., CDL as first GLDE node, Federal Services ontology pilot).
- Ensure zero-trust compliance, data residency, and audit-ready lineage standers across all assets.
- Partner closely with the parallel Sr Director - Data Strategy and Governance to make policies technically enforceable.
- Deliver measurable outcomes such as 99% data quality, 100% ontology conformance, scalable KBQ automation, and foundation for 10-15% revenue uplift / 15-25% cost reduction.
Job-Specific Essential Duties and Responsibilities:
Role Purpose:
- Define and institutionalize enterprise data and analytical tradecraft.
- Advance decision intelligence and responsible AI-enabled analytical practices.
- Establish repeatable methods for governed data products, evidence, and insight delivery.
- Improve analytical quality, trust, explainability, and adoption across business and mission contexts.
- Create practical playbooks, patterns, demonstrations, and accelerators that move concepts from pilot to scale.
- Mentor practitioners and convene communities of practice without direct-report responsibility.
Enterprise Data Tradecraft:
- Establish enterprise standards for analytical rigor, evidence quality, reproducibility, and decision support.
- Develop reusable analytical playbooks, reference patterns, operating models, and assessment methods.
- Define what good looks like for insight generation, analytical products, and data-informed decisions.
- Identify recurring delivery challenges and codify practical methods that improve quality and speed.
AI-Enabled Analytics and Decision Intelligence:
- Shape methods for applying generative AI, retrieval-augmented generation, agentic AI, and knowledge graphs within governed analytical workflows.
- Develop evaluation approaches for AI quality, data readiness, traceability, human oversight, and mission fitness.
- Guide responsible adoption of emerging AI capabilities in regulated and high-consequence environments.
- Prototype and demonstrate AI-enabled approaches that clarify value, limitations, risks, and pathways to scale.
Data Product Strategy and Architecture:
- Define practical standa...
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Type: Permanent Location: Omaha, US-NE
Salary / Rate: Not Specified
Posted: 2026-10-07 10:41:08
