Validate ETL/ELT processes, data pipelines, and data warehouses/lakes for accuracy, completeness, and performance.

- Develop and execute manual and automated test cases for data validation.

- Create and maintain SQL queries to test transformations, aggregations, and business rules.

- Ensure data quality standards (accuracy, consistency, timeliness, integrity) are met.

- Perform source-to-target data validation, schema validation, and reconciliation testing.

- Collaborate with data engineers and developers to resolve defects and improve pipeline reliability.

- Validate data in reports, dashboards, and BI tools against backend sources.

- Participate in test planning, estimation, and reporting activities.

- Document test cases, data quality checks, and test outcomes clearly.

- Contribute to continuous improvement of test automation for data quality.

Required Skills & Experience :

- Strong SQL skills (complex joins, aggregations, data validation queries).

- Hands-on experience testing ETL processes, data warehouses, or data pipelines.

- Experience with relational databases (e.g., Oracle, SQL Server, PostgreSQL, MySQL).

- Knowledge of data formats (CSV, JSON, Parquet, Avro, XML).

- Familiarity with data integration tools (Informatica, Talend, SSIS, dbt, or similar).

- Proficiency in data validation frameworks or test automation tools (e.g., Pytest, dbt test, Great Expectations).

- Exposure to cloud platforms (AWS, Azure, GCP) and data services (BigQuery, Redshift, Snowflake, Databricks).

- Understanding of data governance, lineage, and data quality dimensions.

- Experience working in Agile/Scrum environments.

Desirable Skills :

- Scripting or programming knowledge (Python, Java, or similar) for test automation.

- Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI).

- Familiarity with BI tools (Tableau, Power BI, Looker, Qlik).

- Experience with API testing for data services.

- Knowledge of statistical data validation techniques and anomaly detection.