Data Architect Principal, Analytics Pipeline; FWA)

Regular Clearance Level Must Currently Possess:
None Clearance Level Must Be Able to Obtain:
None Public Trust/Other

Required:

None Job Family:
Data Science and Data Engineering

Job Qualifications:

Skills:

Data Flows, Design, ETL Architecture, Leadership, System Designs

Certifications:

None

Experience:

8 + years of related experience US Citizenship

Required:

No

Own your opportunity to turn data into measurable outcomes for our customers’ most complex challenges. As a Data Architect Principal at GDIT, you’ll power innovation to drive mission impact and grow your expertise to power your career forward. This role exists to design the infrastructure that turns new Partner data, as it lands in a multi-billion-record, multi-payer healthcare claims warehouse (the HFPP Trusted Third Party), into automated fraud, waste, and abuse (FWA) alerts.

You'll own the technical standards that infrastructure gets built and extended against, with real sign-off on the pipeline's architecture and technology choices, and you'll build the hardest parts of it yourself while working alongside a Data Engineer responsible for building and monitoring this pipeline. Fraud-signal methodology stays with Data Science, and PII/PHI handling policy stays with a separate compliance function;

your sign-off covers the pipeline architecture itself. You'll report through the analytic leadership and work alongside several technical and investigative teams. HOW A DATA ARCHITECT PRINICPAL WILL MAKE AN IMPACT:
Design the architecture for the event-driven pipeline that carries new Partner data from the HFPP warehouse into deployed fraud signals and out to Partner alerts: the data flow, the integration points with the warehouse and the HFPP Portal's analytics extension, and the AWS and Snowflake choices that hold it together. Own sign-off on that architecture and any material change to it.

If a new signal's requirements would strain the design, that decision runs through you. Set the standards the pipeline is built to; how it absorbs a spike in Partner data, what happens when a signal's scoring job fails partway through, how a new signal gets onboarded without destabilizing the ones already live. Build the hardest pieces yourself. Proof-of-concept integrations, the trickiest parts of the event-driven design, anything you don't want to hand off until the pattern's actually proven.

This isn't a paper-architecture role. Design within the data-security and PII/PHI constraints a separate compliance function sets for this claims data. Work with Data Science on how an approved fraud signal's logic gets represented in the pipeline, and track the government review and approval cadence closely enough that the architecture can absorb new signals on schedule.

Bachelor's degree in computer science, engineering, or a related field, or equivalent hands‑on experience. 8+ years designing and owning production data architecture, including at least one event-driven or streaming pipeline you built and ran at real scale. Deep hands‑on experience with AWS and Snowflake, including designing event‑driven ingestion patterns that reliably feed a warehouse‑native scoring/analytics layer. Strong Python and SQL, enough to build the pieces you design yourself rather than only diagram them.

Experience designing systems that operate under real data‑security/compliance constraints (PII/PHI or comparable sensitive data), working with a separate security function rather than owning that policy. A track record of setting or reviewing architecture standards other engineers are held to, including saying no to a design that misses the bar. Comfortable working independently in a fully remote environment with sign‑off…