Key Responsibilities :
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.(ref : hirist.tech)