Data QA Engineer, Data Products

About the Role

We're building a first-of-its-kind data product for the ABA (Applied Behavior Analysis) therapy industry, and we need someone who obsesses over whether the numbers are right. This role is the quality gate between our metric specifications and what customers see. You will validate business logic, catch edge cases, and build the repeatable QA processes that let us ship with confidence. You'll work closely with our data engineering and product leaders to ensure that every metric we release is accurate, defensible, and trustworthy. This is not UI testing. This is making sure the math and logic holds up across many organizations, payer types, and complex clinical billing data. This is a 9-month contractor role with a clear path to full-time for the right person.

What You'll Do

• Review detailed business and metric specifications — including inclusion/exclusion rules, normalization logic, and segmentation criteria — and translate them into structured test cases
• Write SQL validation queries against large healthcare datasets to confirm that calculation logic produces correct, expected outputs
• Identify edge cases in statistical outputs such as medians, percentile benchmarks, and segmented comparisons across peer groups
• Build and maintain repeatable QA processes and documentation, including traceability between requirements, test cases, and validated results
• Investigate data anomalies and logic discrepancies, determine root causes, and communicate findings clearly to engineering and product teams
• Contribute to the development of AI-assisted QA workflows as the team matures its validation approach
• Document test results, decisions, and trade-offs so that every release has a clear evidence trail

What We're Looking For

• 3+ years of experience validating business logic, calculation accuracy, or data quality in data products, analytics platforms, or BI reporting environments
• Strong SQL skills. You can write complex queries to validate aggregations, joins, filtering logic, and statistical calculations against source data
• Python skills for data validation strongly preferred
• Experience working with detailed business requirements or metric specifications and translating them into test plans
• You think in edge cases, boundary conditions, and unexpected data patterns
• Clear written communication. You can explain what you tested, what you found, and why it matters
• Healthcare, insurance, or billing data experience strongly preferred
• Comfort working in a small, fast-moving team where you'll have significant ownership and influence over how QA is done

Nice to Have

• Familiarity with benchmarking products or peer comparison analytics
• Experience with Power BI or similar BI tools
• Exposure to AI-assisted testing or validation workflows