Data Analyst/QA Engineer

About the role

STS is looking for a Data Analyst / QA Engineer to join a federal data engineering team. You will play a critical quality gate role on a federal data platform, ensuring data accuracy and software quality across a high-stakes environment where data integrity directly supports government operations. You will directly support the agency's Independent Verification and Validation (IV&V) process and serve as the critical checkpoint between build and production deployment. Meticulous attention to data accuracy and strong SQL skills are prerequisites for this position. This position is contingent upon contract award. The Data Analyst / QA Engineer at STS will: Design, develop, and maintain automated data quality checks using AWS Glue data quality tools (including Deequ ) to validate completeness, accuracy, record counts, field-level integrity, and domain conformance for all datasets ingested into the platform Perform schema validation for applicable filings prior to ingestion; manage data load exceptions with written approval from Data Stewards; implement custom data quality checks as defined by Data Officers and Data Stewards at the dataset level Monitor production ETL pipelines and ensure ETL Load Reports are populated in real-time and ETL Gap Reports are updated on a weekly basis covering all gaps from the inception of the initial ingest process; ensure zero data loss and complete data transfers Create, maintain , and execute test cases and automated tests that verify all user-facing platform functionality; build unit and integration tests; ensure all new code achieves the minimum 90% test coverage threshold Conduct static code analysis and dependency analysis to identify bugs, vulnerabilities, and data quality risks early in the development lifecycle; run security scans at least once per sprint as part of the Definition of Done Support the agency's IV&V process: prepare IV&V Questionnaires, coordinate test submissions, and ensure all findings are fully addressed and resubmitted within required timelines Support User Acceptance Testing (UAT) coordination; track and report on IV&V and UAT iteration counts per reporting period Validate that ETL metadata, data models, data dictionaries, and all required documentation are complete, accurate , and available in the agency data catalog prior to deployment Support ad hoc data requests by developing and validating optimized queries, materialized views, or reports Maintain the team's performance metrics dashboard including velocity, sprint burn-down/burn-up, code coverage, and backlog traceability in real-time Collaborate with Data Officers, Data Stewards, and the IV&V team to resolve data quality findings across the platform dataset portfolio Participate in 2-week sprint ceremonies, quarterly PI planning, backlog refinement, and agile delivery using JIRA and GitHub Education and Experience: Required Bachelor's degree or higher in Computer Science, Information Systems, Data Analytics, or a related field 3-5 years of experience in data quality engineering, QA engineering, or data analytics in a technical data-intensive environment Proficiency in SQL for data validation, gap analysis, record count reconciliation, and ad hoc query development Experience with Python for test scripting and data analysis Experience with AWS Glue data quality tools or equivalent frameworks ( Deequ , Great Expectations) Hands-on experience with CI/CD-integrated automated testing frameworks; ability to build unit and integration tests achieving 90% code coverage Experience supporting IV&V, UAT, or formal government acceptance testing processes; direct federal program experience is strongly preferred Familiarity with schema validation for XML datasets Experience with Trino, Athena, or equivalent query engines for validation against large-scale data lake tables Ability to produce complete test documentation in