Director, Data Engineering - Data & AI Platform

Arlington, VA

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Kapitus is building a next-generation enterprise data and AI capability multi-year modernization program that replaces hundreds of legacy analytics workflows with governed, reusable business data products on a modern cloud data platform, stands up an AI/ML platform, and moves the organization from producing reports to producing decisions.

We are looking for a Director of Data Engineering to serve as the senior technical leader for this program: the person who owns how the platform is engineered. You will set the architecture and engineering standards for a Snowflake-centered data platform, lead internal and partner engineering teams across onshore and offshore locations, and stay hands‑on enough to review a pull request, challenge a data model, and unblock a pipeline yourself.

This is a player‑coach role — you will spend real time in the code and the designs, not only in meetings.

You report directly to the program executive leading the Data & AI organization and serve as the technical delivery lead for the transformation program. You are the counterpart to the Technical Program Manager: the TPM owns the integrated plan, the gates, and the commercial controls; you own the architecture, the engineering quality, and the teams that build. Together you make sure what ships is well‑built, evidenced, and accepted — not merely finished.

• Own the engineering and solution architecture of the data platform within approved enterprise and data architecture standards including layered warehouse design, data modeling, ingestion and transformation patterns, orchestration, environment strategy, and promotion controls and keep delivery conform ant to it.
• Translate approved data architecture, MDM, ontology, semantic, and data-contract standards into enforceable engineering patterns so identity, meaning, metrics, and relationships remain consistent across products.
• Set and enforce engineering standards: repository structure, branching and CI/CD, code review, testing and data quality checks, naming and documentation, and performance and cost discipline.
• Lead design reviews and architecture decision forums; document decisions, manage exceptions with owners and expiry dates, and prevent conformance debt from silently accumulating.
• Design for reuse: shared ingestion frameworks, certified transformation patterns, common serving structures, and semantic consistency - so each delivery wave gets faster, not just bigger.
• Stay technically hands‑on: review critical code and designs, prototype or intervene on the highest‑risk components when needed, tune warehouse performance and cost, and establish the engineering bar through direct technical engagement.
• Lead the engineering build of governed business data products end to end: source onboarding, ELT pipeline…