Lead - ETL Testing

Neurealm; Chennai District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Neurealm in Chennai, Tamil Nadu, India seeks an experienced QA lead to design and drive comprehensive test strategies for ETL/ELT data pipelines. You will validate Delta Tables, notebooks, SQL and PySpark transformations, coordinate end-to-end data testing, and support data migration and reconciliation across systems.

Applicants should have 7-14 years of hands-on testing experience, strong analytical skills, and a track record of automation within CI/CD environments using Jenkins, Azure DevOps

Qualifications

• 7-14 years of experience in QA/testing or data validation.
• Strong experience in end-to-end testing of ETL/ELT pipelines.
• Proficiency with data validation, reconciliation and data quality checks.

Responsibilities

• Develop test strategies, plans, cases, scripts and execution reports.
• Lead QA activities across SDLC and Agile frameworks.
• Validate Delta Tables, Notebooks, SQL and PySpark transformations in Databricks.
• Automate testing and integrate into CI/CD pipelines (Jenkins, Azure DevOps or GitHub Actions).

Skills

ETL/Data Testing

Data Validation

Troubleshooting

PyTest

Tools

AbInitio

SSIS

DataStage

Databricks

Jira

Azure DevOps

Quality Center

Jenkins

GitHub Actions

Job description

Experience Required

7 - 14 years

Location

Chennai, Tamil Nadu, India (CHN)

Job Summary

• Develop comprehensive test strategies, test plans, test cases, test scripts and test execution reports.
• Execute software and data testing including functional, end-to-end, data migration quality assurance, regression and UAT support to ensure flawless system performance and data integrity.
• Lead end-to-end QA activities throughout the Software Development Life Cycle (SDLC) and Agile methodologies.
• Test ETL/ELT pipelines developed using Databricks and cloud-based data processing frameworks, requiring hands-on experience validating Delta Tables, Notebooks,SQL and PySpark transformations.
• Ability to prepare mock data sets as required for testing scenarios.
• Verify data quality checks within ingestion and transformation pipelines.
• Perform source-to-target reconciliation with Data Migration Testing experience
• Perform report reconciliation between legacy reporting systems and new analytics platforms.
• Prepare QA metrics, test status reports and release readiness assessments.
• Improve test coverage through automation and shift-left testing practices.
• Verify KPI calculations, measures, aggregations, filters and drill-down functionality.
• Ensure complete traceability between business requirements and testing artifacts.
• Conduct Root Cause Analysis (RCA) for defects and collaborate with development teams for resolution.
• Drive defect lifecycle management using tools such as Jira, Azure DevOps or Quality Center.
• Manage defect lifecycle and coordinate issue resolution with development teams.
• Integrate automation testing within CI/CD pipelines using Jenkins, Azure DevOps or GitHub Actions.
• Participate in sprint ceremonies and release validation activities.
• Experience with Master Data Management and one or more tools like Azure DevOps, Jira, Quality Center

Mandatory Skills

ETL/Data Testing, Data Validation and reconciliation, Strong analytical & troubleshooting skills, Hands-on exposure to validating data across enterprise ETL platforms (AbInitio, SSIS, DataStage) and automating data quality checks using Pytest or similar testing frameworks.

Good-to-Have Skills

• Healthcare Domain Experience with Healthcare Reporting & Analytics, Provider Data Validation,Clinical Data Testing, Claims Data Validation and understanding of Databricks ecosystem
• Experience working on large-scale cloud data migration, modernization or data warehouse transformation programs.
• Ability to understand complex data workflows and troubleshoot issues within data pipelines.
• Knowledge of cloud based data platforms such as Azure or AWS.
• Strong understanding of Agile Scrum Methodologies and proactive approach towards identifying risks and quality improvements.