ETL / Data Automation/ Selenium Python QA Engineer

We are seeking an experienced ETL / Data Automation/ Selenium Python QA Engineer with 4+ years of hands-on experience in validating complex data pipelines, writing advanced SQL queries, and building scalable test automation frameworks using Python and Pytest. In this role, you will ensure data accuracy, integrity, and performance across large-scale data warehouses and ETL processing layers.

Key Responsibilities

• ETL & Data Testing: Design, execute, and maintain end-to-end test cases for ETL processes, data integration, data pipelines, and data migration projects.
• Complex SQL Validation: Write advanced, highly optimized SQL queries to audit, reconcile, and validate large datasets across source and target systems.
• Automation Engineering: Build and scale data testing automation frameworks using Python and Pytest to automate repetitive data validation and pipeline checks.
• Defect & Data Quality Management: Identify, log, and track data anomalies, schema changes, and pipeline failures, collaborating closely with Data Engineers and Analytics teams.
• Data Verification: Validate data transformations, business logic, null checks, duplicate checks, boundary conditions, and schema integrity.
• CI/CD Integration: Integrate data automated tests into modern CI/CD pipelines to ensure continuous data quality monitoring.

Required Skills & Qualifications

• Experience: Minimum 4+ years in Data Quality Assurance, ETL Testing, or Data Automation.
• Advanced SQL: Expertise in writing complex SQL queries (window functions, subqueries, CTEs, heavy JOINs, performance tuning, and set operations).
• Programming & Frameworks: Strong proficiency in Python and hands-on experience with Pytest for creating data testing scripts.
• ETL & Data Warehousing: Deep understanding of ETL concepts, data warehousing principles (Star/Snowflake schemas), and source-to-target mapping.
• Database Systems: Experience working with relational databases (PostgreSQL, MySQL, MS SQL) or cloud data warehouses (Snowflake, Databricks, Redshift, BigQuery).
• Tools: Proficiency with version control systems (Git) and bug-tracking platforms (Jira).

Nice to Have

• Experience testing big data architectures (Apache Spark, PySpark, AWS S3).
• Familiarity with orchestration tools (Apache Airflow, Prefect).
• Basic exposure to cloud platforms (AWS, Azure, or GCP).

Work Location & Selection Process

• Primary Work Location: Kochi (Hybrid work environment).
• Final F2F Interview at the Kochi Office.