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Senior QA Engineer (ETL Testing)

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Onsite

Senior QA Engineer (ETL Testing)

Nielseniq India

Senior QA Engineer (ETL Testing)

Skills

SQL QueriesData EngineeringPythonTesting & QADatabricksPostmanSeleniumPlaywrightData AnalysisGitHubPySparkApache AirflowAzure

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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