Let's begin! Associate QA Automation Engineer (Databricks)

At Moodys we unite the brightest minds to turn todays risks into tomorrows opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they arewith the freedom to exchange ideas think innovatively and listen to each other and customers in meaningful ways. Moodys is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment were advancing AI to move from insight to actionenabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity helping our clients navigate uncertainty with clarity speed and confidence.

If you are excited about this opportunity but do not meet every single requirement please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship lead with curiosity champion diverse perspectives turn inputs into actions and uphold trust through integrity.

We are seeking a highly experienced Software Quality Assurance (SQA) Engineer with 2-5 years of experience and strong expertise in Databricksbased enterprise data platforms.

This role is focused on ensuring data quality accuracy reliability and correctness of data transformations across largescale batch data pipelines built on Databricks and Delta Lake. The SQA Engineer will adopt an automationfirst mindset leveraging Databricks SQL PySparkbased validation frameworks Delta Lake architecture and Databricks workflows and jobs.

The role requires close collaboration with engineering platform and product teams to consistently enforce data quality and transformation validation standards and provide clear quality signoff within an Agile delivery model.

Key Responsibilities

• Data Quality & Transformation Validation
  • Own endtoend data quality validation for Databricks batch pipelines across Bronze Silver and Gold layers (Medallion architecture).
  • Validate completeness accuracy consistency timeliness reconciliation and businessrule correctness using Databricks SQL and Delta tables.
  • Perform datasetlevel recordlevel schemalevel and transformationrule validations.
  • Validate data transformation logic ensuring correctness of:
    • Sourcetotarget mappings
    • Derived and calculated fields
    • Applied business rules and aggregations
  • Ensure validated datasets are reliable consistent and consumptionready for downstream analytics and reporting use cases.
  • Enforce data quality and validation standards consistently across DEV QA UAT and PROD environments.
  • Provide auditable and automated validation evidence for release readiness and data consumption signoff.
• Automation & Test Frameworks
  • Design develop and maintain scalable reusable data validation frameworks using PySpark and Python on Databricks.
  • Automate integration regression and endtoend testing for Databricks:
    • Notebooks
    • Workflows
    • Scheduled jobs
  • Perform sourcetotarget validation including validation of transformation logic applied during batch processing.
  • Continuously improve automation coverage and reduce manual data validation effort.
  • Ensure automation solutions follow performance maintainability and scalability best practices.
• Test Data Management
  • Define and implement test data management strategies for Databricks batch pipelines.
  • Ensure availability of reliable representative and reusable test datasets across environments.
  • Support test data refresh isolation and masking requirements.
  • Collaborate with platform and engineering teams to align on test data governance and access practices.
• Test Management Reporting & Agile Collaboration
  • Design manage and execute test cases using Xray.
  • Track defects execution results and sprint progress using Jira.
  • Maintain test strategies test plans and data validation documentation.
  • Actively participate in Agile/Scrum ceremonies including sprint planning reviews and retrospectives.
  • Communicate data quality metrics risks and release readiness status clearly to stakeholders.
• Stakeholder Collaboration
  • Collaborate closely with data engineers platform teams product owners and architects.
  • Act as a quality advocate across Databricks batch data pipelines.
  • Communicate clearly on quality risks dependencies and remediation plans.

Required Skills & Experience

• 2-5 years of experience in Software Quality Assurance / Data Quality Engineering
• Experience working with largescale enterprise data platforms
• Databricks & Data Platforms
  • Strong handson experience with Databricks SQL and Databricks Python
  • Experience testing Databricks notebooks workflows and jobs
  • Solid understanding of Medallion architecture (Bronze / Silver / Gold)
• Automation & Data Testing
  • Strong PySpark expertise for data validation and test automation
  • Proficiency in Python and SQL
  • Strong understanding of data testing best practices
  • Experience designing integration endtoend and regression testing strategies
• QA & Delivery Practices
  • Experience working in Agile / Scrum environments
  • Xray mandatory for test management
  • Jira mandatory for defect tracking and sprint execution
  • Automationfirst mindset with focus on quality coverage and reliability

Nice to Have

• French or Dutch proficiency
• Experience with Delta Lake (ACID transactions schema evolution time travel)
• Exposure to data governance or enterprise data quality frameworks
• Experience defining data quality metrics KPIs and dashboards

Moodys 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 Moodys Corporation may be asked to disclose securities holdings pursuant to Moodys 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.