• Required Qualifications, Capabilities and Skills

Work on enterprise-scale data and  analytics platforms to ensure the quality, accuracy, and reliability of        business-critical data solutions.

Collaborate closely with business stakeholders, product owners, data engineers, and development teams to        understand requirements and clarify business scenarios.

Translate business requirements and user stories into comprehensive test scenarios, test cases, and validation strategies.

Design and execute functional,   integration, regression, and end-to-end testing for data pipelines and        analytics applications.

Validate large-scale data   transformations, data ingestion processes, and batch workflows across        Hadoop and cloud-based data platforms.

Perform source-to-target data validation, data reconciliation, and data quality testing to ensure data accuracy        and completeness.

Monitor and validate scheduled jobs   workflow executions using Control-M.

Create and manage mock data and test  datasets to support testing requirements.

Leverage automation frameworks and  AI-assisted testing tools to improve testing efficiency and coverage.

Identify, track, and validate defects   while working closely with development and engineering teams to ensure  timely resolution.

Participate actively in Agile ceremonie  including sprint planning, backlog refinement, daily stand-ups, and        sprint reviews.

Ensure testing deliverables are completed    within committed sprint timelines while maintaining high quality        standards.

Contribute to organizational capability  building through knowledge sharing, mentoring, and adoption of testing        best practices.

Skills and Expertise

Minimum 5–8 years of relevant experience  in Big Data Testing, Cloud Data testing, End to End Data Validation, ETL   Testing, or Quality Assurance.

Strong hands-on experience in Hadoop Testing primarily with Hive and validation of data processing workflows on        Cloud environment using Azure Databricks.

Experience performing Hive table        validation, including schema validation, partition validation, data        reconciliation, and source-to-target verification.

Strong proficiency in SQL for data  analysis, validation, and testing activities.

Hands-on experience with PySpark for validating large datasets, transformations, and business rules.

Working knowledge of Unix commands for   file validation, backend testing, troubleshooting, and data        verification.

Experience working  with Control-M for batch job monitoring and workflow validation.

Experience with  Azure Databricks and Spark-based data processing environments on both        ETL/ELT data pipelines.

Ability to analyze business requirementand translate them into effective testing strategies and test cases.

Experience creating mock data and managing test data for various testing scenarios.

Exposure to test automation frameworks  and automation tools.

Experience using AI-powered testing tools   to improve productivity and test effectiveness.

Strong analytical and problem-solving   skills with attention to detail.

Experience working in Agile/Scrum  delivery environments.

Excellent written, verbal, presentation,  interpersonal, and stakeholder management skills.