QA Engineer- Data Engineering

• Validate data pipelines end-to-end: source-to-target checks, transformations, data completeness, accuracy, and reconciliation
• Write and execute SQL queries to test data quality across ETL/ELT workflows
• Design and maintain test plans, test cases, and traceability documentation for data projects
• Build automated data quality checks and integrate them into pipeline workflows
• Test reports and dashboards (e.g., Power BI/Tableau) against source data to verify metrics and business logic
• Perform smoke, regression, and integration testing after pipeline deployments and schema changes
• Support functional, regression, and UAT testing on other projects (web/app) when required
• Log, track, and verify defects; work closely with data engineers and developers through resolution
• Participate in requirement reviews to identify testability gaps and edge cases early
• Contribute to improving QA processes, standards, and documentation across teams

• Experience testing data pipelines, ETL, or data warehouse projects
• Strong SQL skills — able to write complex queries for data validation independently
• Understanding of data warehousing concepts (schemas, transformations, incremental loads, slowly changing dimensions)
• Experience with test management and defect tracking tools (e.g., Jira)
• Ability to read and understand pipeline logic and data models to design meaningful tests
• Strong attention to detail and ability to switch context across multiple projects
• Good communication skills for working with cross-functional teams
• Exposure to Agile/Scrum ways of working

• Experience with Azure data services (Azure Data Factory, Synapse, ADLS)
• Hands-on experience with Databricks (notebooks, Delta Lake, job runs)
• Test automation skills — Python, pytest, or data quality frameworks like Great Expectations or dbt tests
• Experience testing APIs (e.g., Postman) or basic web automation (e.g., Selenium/Playwright)
• Familiarity with CI/CD pipelines (e.g., Azure DevOps, GitHub Actions)

This QA Engineer role focuses on validating data engineering pipelines and data products, with testing support for web and app projects as needed. The work combines SQL-based data checks, automated quality controls, reporting validation, and coordinated defect resolution.

Role at a glance

Skills mentioned

What you will likely work on

• Check data pipelines end to end, including transformations, completeness, accuracy, and source-to-target reconciliation.
• Use SQL to assess data quality across ETL/ELT workflows.
• Create and maintain test plans, test cases, and traceability records for data projects.
• Build automated data checks and connect them to pipeline workflows.
• Validate dashboard and report metrics against source data and business logic.
• Run smoke, regression, and integration tests after deployments and schema updates; assist with web/app functional, regression, and UAT testing as needed.
• Track defects, confirm fixes with engineers and developers, and review requirements for testability gaps and edge cases.
• Help improve QA practices, standards, and documentation across project teams.

Good fit signals

• You have tested data pipelines, ETL processes, or data warehouse projects.
• You can independently write complex SQL queries to validate data.
• You understand warehouse schemas, transformations, incremental loads, and slowly changing dimensions.
• You are comfortable interpreting pipeline logic and data models to design relevant tests.
• Clear cross-functional communication and careful context switching are important; Agile/Scrum exposure is useful.