QA Engineer - Data Platforms (Databricks)

PassFort; Brussel Hoofdstad; Sur place; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Moody's is seeking a Data Quality Engineer to validate enterprise data platforms built on Databricks. You will own end-to-end validation across Bronze, Silver, and Gold layers to ensure data integrity and business rule compliance.

The role emphasizes automated testing with Python and PySpark, data transformations, and source-to-target mappings. You will collaborate with engineering, platform, product, and business stakeholders to improve testing standards and quality.

Qualifications

• Experience validating data solutions on Databricks.
• Strong SQL, Python, and PySpark with automated testing experience.
• Knowledge of data quality principles and data pipelines.

Responsabilités

• Own end-to-end validation of data pipelines across Bronze, Silver, and Gold layers.
• Develop scalable automated testing frameworks using Python and PySpark.
• Validate data transformations and source-to-target mappings across complex datasets.
• Monitor testing progress and report quality metrics.
• Collaborate with engineering, product, and business stakeholders.

Connaissances

SQL

Python

PySpark

Data quality principles

Agile

Stakeholder management

AI concepts

Formation

Bachelor's degree in Computer Science/Engineering/Data Science

Certifications in testing/cloud/data engineering preferred

Outils

Databricks

Delta Lake

Jira

Xray

Notebooks

Description du poste

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling 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.

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 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. Proven ability to leverage AI‑powered solutions to improve testing effectiveness while maintaining awareness of responsible and ethical AI practices

Education

• 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

Responsibilities

• 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

About The Team

Our Data Engineering and Analytics team is responsible for delivering scalable, reliable, and high-quality data platforms that power critical business insights and decision-making across Moody's. The team partners closely with technology, product, and business stakeholders to build modern cloud-based data solutions, improve data governance and quality standards, and enable trusted analytics at scale. By joining our team, you will work with advanced data technologies, including Databricks and cloud-native platforms, while contributing to the adoption of AI-enabled solutions that improve operational efficiency, innovation, and data-driven decision making across the organization.

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.