ETL Tester

Job Information

About Us

Kumaran Systems: Engineering Future-Ready Transformations Since 1992, Kumaran Systems has been helping enterprises bridge the past and the future, reimagining legacy systems and embracing AI-led possibilities. With over 34 years of delivery excellence, we specialise in re-engineering core applications, driving cloud transformation, and building automation-first, GenAI-enabled ecosystems across the Banking and Financial Services, Insurance, Telecom, and Automotive sectors. We help organisations not just upgrade their systems but reimagine what’s possible.

Why Kumaran?

We blend engineering discipline with AI innovation, helping clients modernise with confidence, automate with clarity, and scale with purpose. Our global delivery model ensures agility, responsiveness, and seamless collaboration, with clients always at the heart of every engagement. At Kumaran, we don’t just solve problems, we engineer future-ready transformations.

Job Description

Role:

ETL / Data QA Automation Engineer

Experience:

4–8 years

Key Responsibilities:

• Design and execute ETL/data validation test cases covering source-to-target data flows, transformations, mappings, and reconciliation.
• Develop and maintain Python-based automation scripts/frameworks for data and ETL testing.
• Perform database validation using SQL across source, staging, and target systems.
• Automate data quality checks, reconciliation, completeness, accuracy, and exception validations.
• Integrate automated tests with CI/CD pipelines and generate test execution reports.
• Analyze defects, perform root-cause analysis, and work closely with developers/data engineers for resolution.

Required Skills:

• Strong experience in ETL/Data Testing and SQL.
• Good hands-on experience with Python and test automation.
• Experience with databases such as SQL Server, PostgreSQL, or Snowflake.
• Knowledge of ETL tools such as Informatica, Talend, SSIS, or equivalent.
• Experience with Git, CI/CD, and automation frameworks.
• Good understanding of data validation, reconciliation, and data quality concepts.