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QA Engineer - Databricks

Moody's
Bengaluru, IND
Full-time
Mid · 4+ years experience
Onsite
Discovered 3 weeks ago
Databricks SQLPythonPySparkSQLXrayJira

Full Job Posting

Skills and Competencies

  • 4+ years of experience in Software Quality Assurance or Data Quality Engineering within large-scale enterprise data platforms
  • Strong hands-on experience with Databricks SQL and Python, including testing of notebooks, workflows, and scheduled jobs
  • Solid understanding of Medallion architecture (Bronze, Silver, Gold) and data lifecycle validation practices
  • Expertise in PySpark for data validation, test automation, and building scalable testing frameworks
  • Strong proficiency in SQL and data validation techniques across dataset, record, schema, and transformation levels
  • Experience designing integration, end to end, and regression testing strategies with an automation first mindset
  • Experience working in Agile or Scrum environments with tools such as Xray for test management and Jira for defect tracking
  • Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to implement AI-powered solutions to solve business challenges. Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use.

Education

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field

Responsibilities

  • Ensure data quality, reliability, and correctness across large scale Databricks batch pipelines through automated validation and testing practices.
  • Own end to end data quality validation for Databricks pipelines across Bronze, Silver, and Gold layers
  • Validate data completeness, accuracy, consistency, timeliness, and business rule correctness using Databricks SQL and Delta tables
  • Perform dataset, record, schema, and transformation level validations including source to target mapping and derived field checks
  • Design, develop, and maintain reusable and scalable data validation frameworks using PySpark and Python on Databricks
  • Automate integration, regression, and end to end testing for notebooks, workflows, and scheduled jobs
  • Define and implement test data management strategies including data refresh, masking, and environment isolation
  • Manage test cases, execution, and defect tracking using Xray and Jira while maintaining test documentation and validation evidence
  • Collaborate with engineering, platform, and product teams to enforce data quality standards and communicate risks and release readiness

About the Team

Our Data Platform and Engineering team is responsible for building and maintaining scalable, high quality data solutions that power critical business insights and analytics. By joining this team, you will collaborate with cross-functional experts across engineering, product, and platform functions to drive innovation in modern data architectures using Databricks and Delta Lake. The team is committed to adopting automation, advanced analytics, and responsible AI practices to ensure reliable and high performing data ecosystems.

Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law. Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.

Requirements

  • 4+ years of experience in Software Quality Assurance or Data Quality Engineering within large-scale enterprise data platforms
  • Strong hands-on experience with Databricks SQL and Python, including testing of notebooks, workflows, and scheduled jobs
  • Solid understanding of Medallion architecture (Bronze, Silver, Gold) and data lifecycle validation practices
  • Expertise in PySpark for data validation, test automation, and building scalable testing frameworks
  • Strong proficiency in SQL and data validation techniques across dataset, record, schema, and transformation levels
  • Experience designing integration, end to end, and regression testing strategies with an automation first mindset
  • Experience working in Agile or Scrum environments with tools such as Xray for test management and Jira for defect tracking
  • Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to implement AI-powered solutions to solve business challenges. Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use.

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