QA Data Engineer

Required Skills & Experience

• 5-7 years of QA experience with at least 3 years focused on data warehouse, ETL/ELT, or pipeline testing.
• Hands‑on testing experience on Databricks (notebooks, SQL, basic PySpark).
• Strong Python skills for writing validation scripts, iterating across columns/tables, and producing automated comparison reports.
• Advanced SQL skills, including complex joins, aggregations, window functions, and reconciliation patterns.
• Demonstrated ability to write detailed test plans and test cases independently.
• Experience performing root cause analysis on data issues and producing structured RCA documentation.
• Experience working in Jira-driven QA workflows.
• Strong communication skills; comfortable working directly with business stakeholders to clarify requirements.

• Experience with Azure Synapse / Azure Data Warehouse.
• DAX query authoring against tabular cubes (SSAS Tabular / Azure Analysis Services / Power BI).
• Snowflake experience.
• Exposure to AI workflows or AI‑assisted automation for test case generation or data validation.
• Experience with synthetic and controlled test data generation.

The Senior QA Data Engineer is a strong individual contributor responsible for the most complex test design and validation work on the team. This role authors detailed test plans and test cases for new transformations and ingestion processes, executes advanced data validation across base tables and tabular cubes, and leads root cause analysis on high-impact failures. The Senior QA Engineer contributes meaningfully to the team's Python/Databricks automation assets and supports both DP 1.0 and DP 2.0 environments.

• Create comprehensive test plans and test cases for new transformations and ingestion processes, working from minimal business-supplied input.
• Execute advanced data validation: verify code correctness against base tables, confirm key fields contain the correct data, and reconcile system outputs against business and vendor expectations (e.g., sales figures, account roles).
• Write complex SQL and Python validation scripts to compare data warehouse sources against tabular cube outputs, including completeness, sum totals, null percentages, and key-field checks.
• Author DAX queries against tabular cubes to extract data for comparison against the Azure Data Warehouse source.
• Lead testing for new transformations and migrations across DP 1.0 (Databricks + Synapse) and DP 2.0; assess impact on downstream cubes and reports.
• Conduct root cause analysis on data quality incidents; document findings, impact, and remediation, and partner with development teams on resolution.
• Contribute to and extend the team's automated test scripts and frameworks under the Technical Lead's direction.
• Use Jira to receive assignments and notifications, kick off test plans, and track results.
• Mentor QA Engineers/Testers on SQL, Python, validation techniques, and test planning.