About the role
- 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)