We are seeking a highly experienced Software Quality Assurance (SQA) Engineer with 8+ years of experience and strong expertise in Databricks‑based enterprise data platforms. This role is focused on ensuring data quality, accuracy, reliability, and correctness of data transformations across large‑scale batch data pipelines built on Databricks and Delta Lake. The SQA Engineer will adopt an automation‑first mindset, leveraging Databricks SQL, PySpark‑based 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 sign‑off within an Agile delivery model.
Key Responsibilities
Own end‑to‑end data quality validation for Databricks batch pipelines across Bronze, Silver, and Gold layers (Medallion architecture). Validate completeness, accuracy, consistency, timeliness, reconciliation, and business‑rule correctness using Databricks SQL and Delta tables. Perform dataset‑level, record‑level, schema‑level, and transformation‑rule validations. Validate data transformation logic, ensuring correctness of source‑to‑target mappings, derived and calculated fields, applied business rules and aggregations. Ensure validated datasets are reliable, consistent, and consumption‑ready 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 sign‑off.
Design, develop, and maintain scalable, reusable data validation frameworks using PySpark and Python on Databricks. Automate integration, regression, and end‑to‑end testing for Databricks: notebooks, workflows, scheduled jobs. Perform source‑to‑target 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.
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.
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.
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
8+ years of experience in Software Quality Assurance / Data Quality Engineering.
Experience working with large‑scale enterprise data platforms.
Databricks & Data Platforms: strong hands‑on 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, end‑to‑end, 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; automation‑first 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.
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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