Senior Principal, Chief of Data Tradecraft

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...