Data QA Engineer

Position Summary:Title: QA Analyst or Data TesterDuration: 12 Months - Long Term Location: Washington, DC 20433 (4 days onsite from day 1)Overview: Responsible for independently verifying that data transformations match business requirements, historical data is accurately archived, and the overall system is free of data defects prior to production go-live.Scope of Work:Pipeline Development and Implementation:Review STTMs, Data Quality Rulebooks, and business requirements to develop comprehensive test plans and edge cases.Design automated test scripts for data pipelines and migration loads.Solution Design and Optimization:Execute complex SQL queries to compare legacy source data against AWS/Azure target data to ensure 100% schema and value alignment.Validate the successful application of data masking, PII encryption, and RBAC rules.Test the rollback protocols and data refresh mechanisms.Stakeholder Engagement and Change Management:Coordinate closely with the Business Analyst to plan and execute User Acceptance Testing (UAT) cycles.Assist client business users during UAT by explaining testing dashboards, reports, and how to verify data setParticipate in triage meetings with developers to prioritize defectGovernance, Ethics, and Risk:Log, track, and manage data defects in Jira/Azure DevOps, tracing them back to specific pipeline code or mapping errors.Validate that read-only archives comply with clients immutability requirements.Documentation and Reporting:Generate formal pre-migration and post-migration reconciliation reports.Document test execution results to serve as official audit artifacts for the Clients compliance teams.Required Qualifications and Experience:• 3-5 years as a QA Analyst or Data Tester, specifically focused on data warehouses, complex ETL pipelines, or massive data migrations.• Advanced to Expert SQL is mandatory.• Experience with automated data testing frameworks (e.g., dbt tests, Great Expectations) and bug tracking tools.• Highly analytical mindset, uncompromising stance on data quality, and the ability to systematically investigate root causes of data discrepancies.• Advanced to Expert SQL is mandatory.• STQB Certification or equivalent QA credentials. (Preferred)• Bachelor’s or Master’s in Computer Science, Data Engineering, or a related quantitative field.“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”