Cloud Database Engineer

Leidos

2d•$107,900 - $195,050•Onsite

About The Position

The Behavioral Health and Readiness Division at Leidos Health & Services Sector is looking for a Cloud Database Engineer to help with developing secure and high-performing data solutions for our Navy customer (the Warfighter Performance Department at the Naval Health Research Center in San Diego, CA). Our program is a long-established sleep and fatigue lab with a substantial accumulated dataset. We conduct funded research studies as well as non-research data collections and analyses that provide commands with near–real-time insight into warfighter readiness to inform operational decisions. Our data spans wearable, survey, and metadata sources across multiple active studies involving U.S. Navy and Marine Corps personnel. We have established space, compute, S3 storage, Databricks, Qlik Sense, and GitLab in the Advana IL5 environment. We are seeking a team member to design and build — in close collaboration with our team — a maintainable, extensible data platform that ingests, validates, transforms, and exposes our data for analysis and reporting. The ideal candidate brings strong technical leadership, hands-on database development skills, and the ability to collaborate across teams to deliver innovative, efficient, and highly usable data Systems.

Requirements

Requires BS degree and 8 – 12 years of prior relevant experience or Masters with 6 – 10 years of prior relevant experience.
May possess a Doctorate in technical domain.
Must possess or be able to obtain an active Secret Security Clearance.
US Citizenship is required.
Requires expert knowledge of and ability to apply advanced technical principles, theories, and concepts.
Familiarity with the Advana ecosystem and Jupiter-managed data ingest frameworks.
Prior experience engineering data pipelines
Able to develop solutions to complex technical issues and problems that impact multiple area or disciplines.

Nice To Haves

Prior experience engineering data pipelines specifically for commercial wearable devices (Oura, Whoop, Garmin) or survey platforms (Qualtrics)
Experience working with military health research

Responsibilities

Build a production data platform on Advana (Databricks + S3 medallion architecture) that supports current and future studies.
Deliver a system the team can maintain and extend independently after handoff — particularly the ability to onboard new wearable vendors using a documented, repeatable pattern.
Build iteratively and live, with the lab using each feature or pipeline as it is ready.
Engage hands-on with our existing data and stakeholders to propose and iterate on a medallion architecture (bronze/silver/gold), naming conventions, partitioning, and schema-evolution strategy.
Bronze layer shall preserve raw, unmutated vendor payloads as a hard requirement.
After major pipeline updates, either all downstream silver/gold data shall be regenerated, or a separate tracking table (or tables) shall document the pipeline version applied to each piece of data from raw onward.
Build pipelines (PySpark / Databricks-native tooling, contractor's choice) that trigger on landing of new files in the bronze bucket from Jupiter-managed vendor ingests, manual uploads, and other sources.
Vendors in scope include Whoop, Oura, Garmin, Somfit, and Qualtrics.
Implement automated QC checks and reports as a standing part of every pipeline, so vendor-side breakages or anomalies (beyond simple type checks) are caught early and surfaced to the lab.
QC shall include statistical checks of the data (e.g., distributional shifts, unexpected gaps, out-of-range values) in addition to structural and type checks.
Produce and demonstrate a documented, repeatable pattern for onboarding new wearable vendors.
The pattern's adequacy will be judged by its successful application to the in-scope vendors.
Integrate historical data with input from the team and other SMEs.
Ensure gold-layer tables are structured for downstream consumption by Qlik Sense dashboards and Databricks notebooks (including future ML/LLM use cases).
Maintain living documentation (architecture, data model, vendor onboarding playbook, operational runbook) throughout.
Conduct working knowledge- transfer sessions with staff over the course of the cloud pipeline development.
Weekly or bi-weekly syncs with lab technical staff; demos at meaningful milestones rather than fixed calendar dates.
Coordinate with the Navy Jupiter/Advana team where pipelines depend on Jupiter managed vendor ingests.