-
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: Savannah, US-GA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:40:02
-
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-GA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:40:01
-
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: Atlanta, US-GA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:40:00
-
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: Hartford, US-CT
Salary / Rate: Not Specified
Posted: 2026-10-07 10:40:00
-
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: Wilmington, US-DE
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:59
-
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: Dover, US-DE
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:58
-
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: Tampa, US-FL
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:58
-
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: Jacksonville, US-FL
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:54
-
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: Miami, US-FL
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:51
-
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: San Diego, US-CA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:50
-
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: Sacramento, US-CA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:49
-
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: Denver, US-CO
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:49
-
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: San Francisco, US-CA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:48
-
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: Colorado Springs, US-CO
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:47
-
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: Bridgeport, US-CT
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:47
-
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: Fort Smith, US-AR
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:46
-
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: Little Rock, US-AR
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:45
-
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: Los Angeles, US-CA
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:44
-
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: Phoenix, US-AZ
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:44
-
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: Mobile, US-AL
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:40
-
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: Tucson, US-AZ
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:38
-
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: Birmingham, US-AL
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:34
-
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: Montgomery, US-AL
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:31
-
Maximus TCS (Technology and Consulting Services) Internal Job Profile Code: TCS215, T5, Band 8
Job-Specific Essential Duties and Responsibilities:
- Responsible for conducting analysis of transition planning, intelligence information requirements, and developing architecture baselines.
Duties may include providing research and assistance with implementation of community policies and guidance.
- Support business process improvements or systems analysis for missions, systems, and fiscal requirements.
- Resolve highly complex problems using significant application of technical knowledge, conceptualizing, reasoning and interpretation.
- Develop solutions that are highly innovative and achieved through research and integration of best practices.
- Make recommendations to aid sponsor decision-making, to include proposed strategies and/or roadmaps; identified risks, capability and performance gaps, and dependencies; alignment with the strategic framework and priorities; and funding and related program issues
- Assist government client with programmatic goals and objectives, schedules, and reports to the senior-level executives
- Communicate technical topics in a non-technical manner to senior-level audiences
- Monitoring investment compliance
- Developing business cases, concepts of operations, and memoranda agreements
- Monitoring, forecasting, evaluating, and assessing programmatic trends and relationships
- Developing and maintaining project plans and schedules
- Developing integration and transition plans
- Conducting quality reviews of products and services
- Produce deliverables in multiple formats as needed (e.g., Excel, Word, PowerPoint, etc.)
- Develop program assessment processes and methodologies
- Tracking, reporting key progress on, and analyzing cost, schedule, risk and performance
Job-Specific Minimum Requirements:
- TS/SCI with CI Polygraph security clearance
- COMMUNICATION: Requires ability to communicate with executive leadership (internally or client) regarding matters of significant importance to the organization/project.
- KNOWLEDGE: Has in-depth understanding of technical principles, theories, concepts and their application across range of programs.
- EXPERIENCE: Portfolio Management Experience
- Monitor and analyze IT portfolio of programs and their associated capabilities and Services
- Strategic Planning Experience - support the evolution of the Sponsor's strategic direction and priorities
- Extensive experience in collaboration across multiple entities
Preferred Skills and Qualifications:
- Typically requires BS degree and 12+ years of prior relevant experience or Masters with 10+ years of prior relevant experience.
May possess a Doctorate in technical domain Portfolio
- Draft products that reflect strong writing and grammatical skills
- Knowledge of portfolio, program, and project management discipline
- Familiarity with planning, programing, Budgeting, and execution discipline
- Knowledge of key performance indicators...
....Read more...
Type: Permanent Location: Bethesda, US-MD
Salary / Rate: Not Specified
Posted: 2026-10-07 10:39:27
-
Essential Duties and Responsibilities:
- Provide assistance to program clients with completion of paperwork and obtains employment verification documentation.
- Support case managers to obtain attendance documentation.
- Scan and log all client documentation in an accurate and timely manner.
- Ensure activities and processes are in compliance with both company QA standards and applicable contractual standards.
- Enter, transcribe, record, store, or maintain information in written, electronic and magnetic form relating to services, processes and quality systems.
- Coordinate review activities as assigned by management.
- Perform general office duties such as filing, copying, faxing and mail.
- Perform other duties as may be assigned by management.
Minimum Requirements
- High school diploma or equivalent with 0 - 2 years of experience.
- Bilingual proficiency in both English and Haitian Creole.
- Must currently and permanently reside in the Continental US.
Preferred Skills and Qualifications
- Florida state residency.
- Experience working with the Florida Healthy Kids Program.
Home Office Requirements
- Desktop or Laptop that runs Windows, Mac, or Linux (no Chromebooks or tablets).
- OS for Windows - Current release of Windows 10 or newer.
- OS for Mac - Big Sur (11.0.1+); MacOS (10.14) or newer.
- OS for Linux - Ubuntu (18.04).
- Connectivity to the internet via either Wi-Fi or RJ-45 connection for wired network connection to home router (no mobile hotspots).
- Internet speed of 25mbps or higher required (you can test this by going to www.speedtest.net).
- USB plug and play wired headset with a microphone (no Bluetooth, AirPods or wireless gaming headsets).
- Must have a camera.
EEO Statement
Maximus is an equal opportunity employer.
We evaluate qualified applicants without regard to race, color, religion, sex, age, national origin, disability, veteran status, genetic information and other legally protected characteristics.
Pay Transparency
Maximus compensation is based on various factors including but not limited to job location, a candidate's education, training, experience, expected quality and quantity of work, required travel (if any), external market and internal value analysis including seniority and merit systems, as well as internal pay alignment.
Annual salary is just one component of Maximus's total compensation package.
Other rewards may include short- and long-term incentives as well as program-specific awards.
Additionally, Maximus provides a variety of benefits to employees, including health insurance coverage, life and disability insurance, a retirement savings plan, paid holidays and paid time off.
Compensation ranges may differ based on contract value but will be commensurate with job duties and relevant work experience.
An applicant's salary history will not be used in determining compensation.
Maximus will comply with regulatory minimum wage rates and exempt salary thresholds in all instances.
Accommodat...
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Type: Permanent Location: Cheyenne, US-WY
Salary / Rate: Not Specified
Posted: 2026-10-07 10:34:49
