QA Engineer - Data Platforms (Databricks)
Indexed description
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.
Skills And Competencies
- 5+ years of experience in Software Quality Assurance, Data Quality Engineering, or testing enterprise-scale data platforms
- Hands-on experience validating data solutions on cloud-based data platforms, including workflows/jobs orchestration and Delta/Lakehouse-style architectures; experience with Databricks specifically is preferred but not required
- Strong proficiency in SQL, Python, and PySpark with experience developing automated testing and data validation frameworks
- Experience testing large-scale batch data pipelines, data transformations, reconciliation processes, and source-to-target integrations
- Strong understanding of data quality principles, including completeness, accuracy, consistency, timeliness, and business rule validation
- Experience with Agile delivery methodologies and test management tools such as Jira and Xray
- Excellent analytical, problem-solving, communication, and stakeholder management skills
- Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to leverage AI-powered solutions to improve testing effectiveness while maintaining awareness of responsible and ethical AI practices
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical discipline
- Relevant testing, cloud, or data engineering certifications are preferred
- Own end-to-end validation of data pipelines across Bronze, Silver, and Gold data layers to ensure data integrity and business rule compliance
- Design, develop, and maintain scalable automated testing frameworks using Python and PySpark to improve efficiency, coverage, and reliability
- Validate data transformations, schema changes, reconciliation processes, and source-to-target mappings across complex datasets
- Execute integration, regression, end-to-end, and data quality testing for data products, workflows, and scheduled processing jobs
- Define and maintain testing strategies, test cases, test data, execution results, and release validation documentation
- Partner closely with engineering, platform, product, and business stakeholders to identify quality risks and ensure successful delivery outcomes
- Monitor, track, and communicate testing progress, quality metrics, defects, and release readiness using established governance processes
- Contribute to continuous improvement initiatives by identifying opportunities to enhance automation, testing standards, and quality engineering practices
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.
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