Senior Principal, Chief of Data Tradecraft

The Senior Principal, Chief of Data Tradecraft is the enterprise authority for the methods, standards, patterns, and practices used to transform data into actionable insight, sound decisions, mission outcomes, and responsible AI-enabled capabilities. The role combines technical depth, analytical rigor, product thinking, federal mission awareness, and executive communication to improve the quality, repeatability, and impact of data and AI solutions across the organization.

This is a senior individual contributor role. The Chief of Data Tradecraft leads through expertise, influence, facilitation, and mentorship rather than formal people management. The role works across Data Management & Analytics, technology, operations, business segments, and client-facing teams to develop reusable tradecraft, shape high-value data products, guide AI-enabled analytical approaches, and translate complex architectures into practical business and mission outcomes.

This is a remote position.

• 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 work spaces.
• Drive ontology enforcement through JSON Schema + Pydantic validation, TDFPolicy axioms, and Data Guard 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.

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.

• 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.

• 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.

• Define practical standards for domain-oriented, governed, reusable, and discoverable data products.
• Advise teams on product vision, use-case prioritization, roadmap development, and adoption measures.
• Connect business and mission requirements to data architecture, cloud analytics, and platform capabilities.
• Promote data-as-code, product lifecycle, observability, and quality patterns that support reliable reuse.

• Advance the use of ontology, semantic models, metadata, and knowledge graphs to improve context and analytical…