AI & Data Governance Architect

iAdeptive Technologies is hiring a Lead Data Governance, Engineering & AI Architect to own the place where data governance, data engineering, and AI meet — because on this platform they are not separate disciplines. You will design and implement the domain model, the governance framework, the data management framework, and the AI enablement layer as one coherent system for a modern, governed lakehouse serving a federal health agency on AWS Gov Cloud (FedRAMP High).

This is a senior, hands‑on architecture role with real breadth. You will lead the design of a two-tier domain model, stand up the catalog, lineage, and data-quality instrumentation that make data trustworthy, and both govern and help build the AI models that sit on top of it — all under an ISO/IEC 42001 AI management framework. You will lead a small team and serve as the authority who can stand behind data lineage, quality, and AI decisions when an auditor or program leader asks how the platform earns trust.

Governance, Engineering & AI Are One System — You design them as a single coupled system: the domain model shapes the catalog, the catalog feeds lineage, lineage and quality make AI trustworthy, and AI governance closes the loop.

Compute-in-Code, Narrate-with-LLM — Data-quality metrics and governance controls run on deterministic, testable code paths. AI and RPA orchestrate, detect anomalies, and narrate results — they never silently produce the numbers. Keeping the measurement reproducible and the AI separable is a core engineering value here.

Architect Who Builds — You set the reference architecture and stay in the implementation. If it can't be traced, it isn't governed; if you can't build it, you can't lead it.

• Lead the design and implementation of a two-tier domain model as the platform's organizing structure
• Own the data management framework: standards for how data is defined, owned, modeled, retained, and shared
• Establish the metadata and reference-data model the rest of the platform builds against
• Design data modeling for AI — feature and semantic structures and vector/embedding schemas that make data consumable by RAG and generative systems

• Design and implement the data governance framework — catalog, business glossary, and end-to-end lineage
• Build data-quality instrumentation as deterministic, testable controls, using AI and RPA to orchestrate, detect anomalies, and narrate — not to compute the metrics themselves
• Translate data classification, retention, and access policy into enforced, automated controls
• Stand up and lead data stewardship across the domain model

• Support the design, build, and evaluation of AI models (e.g., RAG and generative/agentic capabilities on Amazon Bedrock), partnering with the AI engineering team
• Govern AI under ISO/IEC 42001 — model lineage, evaluation, audit trail, responsible-use controls
• Keep deterministic computation separable from AI-generated narration across data products
• Apply AI and RPA to automate governance and data-management tasks where it raises quality and traceability

• Lead a small team of stewards, engineers, and analysts; set standards, review designs, mentor
• Partner with platform, security, and engineering leads to embed governance and AI controls into pipelines
• Represent data governance and AI assurance in architecture reviews, audits, and oversight

• 10+ years across data architecture, data management/governance, and data engineering, with real ownership of a governance program or reference architecture
• Hands‑on experience designing domain/data models and data management frameworks at enterprise scale
• Deep experience with data catalog and lineage tooling (Open Metadata, Apache Atlas) and data-quality frameworks
• Hands‑on AWS data stack — Glue, Lake Formation, Athena, S3 — and lakehouse table formats (Apache Iceberg, Delta, or Hudi)
• Experience building or governing AI/ML models — ideally RAG or generative/agentic — and a clear point of view on separating deterministic computation from AI narration
• Experience…