Senior Business Analyst / Data Quality Assurance Engineer — Federal Healthcare
Company: Rackner
Location: Location not specified (Remote)
Type: Contract
Remote: Yes
Posted: 2026-07-21
About this role
Location:
Remote
Clearance:
Active DoD Secret clearance
Employment Type:
Full-time
Turn Complex Healthcare Data into Trusted, Mission-Ready Solutions
Join Rackner to improve the quality, reliability, and usability of data supporting mission-critical federal healthcare systems.
This is a QA-focused role with requirements support. You will partner with analysts, engineers, product owners, and subject-matter experts to validate healthcare data, applications, reports, APIs, and integrations across the delivery lifecycle.
You will do more than execute predefined test cases. You will help shape test strategy, build and maintain automated tests, investigate data issues with SQL, and connect requirements to measurable validation outcomes.
Business need → testable requirement → validated data → trusted release
In This Role, You Will
- Own testing activities from planning through defect resolution and release validation.
- Build technical depth across SQL, databases, ETL processes, APIs, and cloud applications.
- Strengthen automated testing, data profiling, traceability, and quality practices.
- Support healthcare systems where accurate, secure, and dependable data matters.
What You’ll Do
- Review business and technical requirements and translate them into clear test conditions, acceptance criteria, and validation strategies.
- Support requirements development through stakeholder discussions, user stories, process analysis, and backlog refinement.
- Build, execute, and maintain automated and manual tests using established tools and frameworks.
- Develop test plans and test cases for healthcare data, applications, reports, APIs, and user interfaces.
- Validate data-ingestion and ETL processes by comparing source data, transformation rules, and target outputs.
- Write SQL queries and joins to investigate data quality, confirm business rules, and identify missing, duplicate, inconsistent, or unexpected records.
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