Data QA Engineer

Position Summary:

Title: QA Analyst or Data Tester

Duration: 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 set
• Participate in triage meetings with developers to prioritize defect

Governance, 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.”