Sr. ETL Tester

Role: Senior ETL Tester
Required Technical Skill Set : ETL Testing, SQL No of Requirements :14 Desired Experience Range: 6-15 years Location : Chennai, Hyderabad, Kolkata Desired Competencies: Must-Have: 1. 5+ years of experience in QA or Quality Engineering roles in data-driven environments. 2. Analyze the data requirements from Source to Target Mapping requirements 3. Hands-on experience with test automation frameworks (e.g., PyTest, Great Expectations, dbt tests). 4. Proficient in SQL for validating data transformations across platforms like Redshift, Postgres, or Snowflake. 5. Familiarity with ETL testing, data ingestion pipelines, and data quality validation techniques. 6. Experience testing and validating BI dashboards and reports, preferably using tools like Tableau. 7. Exposure to CI/CD practices and integration of tests into deployment pipelines. 8. Ability to read and write test cases in Python or other scripting languages. 9. Experience with version control tools (e.g., Git) and JIRA or similar test tracking systems. Good-to-Have: 1. Experience testing unstructured data pipelines, including logs, PDF documents, or application telemetry. 2. Familiarity with data profiling and anomaly detection tools. 3. Exposure to cloud platforms, especially AWS (S3, Redshift, Glue). 4. Understanding of analytical data modeling concepts, including Star Schema and OLAP structures. 5. Knowledge of testing AI/ML models, particularly for consistency, fairness, or accuracy in predictions. Responsibility of / Expectations from the Role 1. Define, implement, and maintain automated test suites to validate data pipelines,transformations, and analytical outputs. 2. Collaborate with data engineers, modelers, and AI engineers to establish and enforce data quality standards and best practices. 3. Conduct functional, regression, and integration testing of ETL jobs, data ingestion pipelines, and model outputs. 4. Develop test strategies to validate structured (OLTP/OLAP) and unstructured data (logs, PDFs,etc.). 5. Build automated tools and dashboards for data profiling, data drift detection, and quality scorecards. 6. Identify root causes of data quality issues and coordinate remediation efforts with engineering teams. 7. Validate data accuracy across Redshift, Postgres, and data marts using SQL and scripting tools. 8. Ensure traceability from business rules and requirements to test cases and final outputs. 9. Participate in Agile/Scrum processes including sprint planning, demos, and retrospective.