Nielseniq India
Senior QA Engineer (ETL Testing)
Skills
2 months ago
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Important information
Contract type:
Permanent
Location:
Chennai, Tamil Nadu, India
Work mode:
Onsite
Published on:
28 August 2026
What they need
Job Summary:
We are seeking a highly skilled and detail-oriented QA Engineer with strong expertise in ETL Testing, Data Validation, Python, PySpark, Databricks, SQL, Selenium, Playwright, and Apache Airflow. The ideal candidate will be responsible for validating end-to-end data pipelines, ensuring data quality, testing large-scale data processing frameworks, and automating functional and regression testing activities across modern cloud-based data platforms.
This role requires a strong understanding of data engineering concepts, automation frameworks, and analytical thinking to ensure the accuracy, reliability, and performance of enterprise data solutions.
Required Skills:
Must-Have Technical Skills
- Strong experience in ETL Testing and Data Warehouse Testing.
- Hands-on experience with Python scripting and test automation.
- Strong knowledge of PySpark and distributed data processing.
- Experience working with Databricks platform.
- Advanced SQL skills with complex query writing and data validation.
- Hands-on experience with Playwright automation framework.
- Strong expertise in Selenium WebDriver.
- Experience testing Apache Airflow workflows and DAGs.
- Experience in API testing using tools such as Postman or Python libraries.
- Strong understanding of Agile/Scrum methodologies.
- Experience with defect tracking and test management tools.
Good-to-Have Skills:
- Azure Data Factory (ADF)
- Azure Data Lake Storage (ADLS)
- Azure DevOps
- CI/CD pipelines
- Git/GitHub
- Data Analytics and Reporting tools
- Performance and Load Testing
Sucess Criteria:
- High-quality validation of ETL and data engineering solutions.
- Increased automation coverage across applications and data pipelines.
- Reduction in production defects through proactive testing.
- Reliable validation of Databricks, PySpark, and Airflow workflows.
- Strong collaboration with development and data engineering teams.
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