Job Description :

Key Responsibilities :

- Design, develop, and maintain automated QA frameworks for Data & Analytics platforms.

- Implement automation strategies for data ingestion pipelines, transformations, ETL/ELT workflows, and data warehouse validation.

- Build scalable and reusable frameworks for end-to-end data validation.

- Develop and maintain DBT tests for schema validation, data quality checks, referential integrity, and business rule validation.

- Validate transformed datasets for accuracy, completeness, and consistency.

- Perform ETL/ELT testing across multiple enterprise data sources.

- Validate source-to-target mappings, reconciliation processes, and transformation logic.

- Automate regression, smoke, reconciliation, and deployment validation testing.

- Integrate testing frameworks with Apache Airflow and CI/CD pipelines.

- Collaborate closely with Data Engineers, BI Engineers, and Analytics teams.

- Implement automated controls for anomaly detection, duplicate checks, and governance requirements.

Mandatory Skills :

- Strong experience in Data & Analytics QA Automation.

- Hands-on experience with DBT test automation and data validation frameworks.

- Strong understanding of Data Engineering concepts and ETL/ELT processes.

- Advanced SQL skills for data validation and analysis.

- Experience with Apache Airflow or similar orchestration tools.

- Experience with cloud-based data platforms (AWS preferred).

- Experience testing enterprise-scale data pipelines and analytics platforms.

- Strong knowledge of Data Warehousing and Dimensional Modeling concepts.

- Excellent troubleshooting and analytical problem-solving skills.

Preferred Skills :

- Databricks, Snowflake, Redshift, or similar modern data platforms.

- CI/CD and DevOps practices for Data Platforms.

- Automated Data Reconciliation and Monitoring tools.

- Experience supporting BI and Analytics reporting environments.