Senior Data QA Engineer for Databricks & AI

Moody's Corporation

Brussel

Sur place

EUR 90 000 - 120 000

Plein temps

14 jours+

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Résumé du poste

Moody's Corporation is seeking a skilled Data Quality Assurance professional to validate enterprise data platforms built on Databricks and ensure accurate, timely data across Bronze, Silver, and Gold layers.

You will design automated testing frameworks in Python/PySpark, validate complex transformations, and collaborate with engineering, product, and business teams to drive quality and release readiness. Strong AI tooling experience is highly regarded.

Qualifications

5+ years in Software Quality Assurance, Data Quality Engineering, or testing enterprise-scale data platforms.
Hands-on experience validating data solutions built on Databricks, including workflows, notebooks, jobs, and Delta Lake.
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.

Responsabilités

Ensure the quality, accuracy, and reliability of enterprise data platforms and batch processing pipelines built on Databricks.
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.