Senior Principal

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 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.

Semantic and Knowledge Enablement:

- Advance the use of ontology, semantic models, metadata, and knowledge graphs to improve context and analytical consistency.

- Connect business concepts, authoritative data, analytical logic, and decision outcomes.

- Partner with architecture and governance teams to make enterprise meaning operational and consumable.

Executive and Client Advisory:

- Serve as a trusted advisor to executives, program leaders, architects, product teams, and federal mission stakeholders.

- Translate complex data and AI concepts into clear decisions, roadmaps, briefings, demonstrations, and written thought leadership.

- Facilitate workshops that align stakeholders on mission needs, analytical opportunities, technical options, and adoption paths.

- Support strategic pursuits and solution shaping where advanced data, analytics, or AI tradecraft is differentiating.

Practice Development and Mentorship:

- Mentor analysts, data scientists, architects, engineers, and product leaders through coaching, design reviews, and working sessions.

- Lead a community of practice that shares methods, lessons learned, exemplars, and reusable assets.

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