quality engineer (auto) for data migration

The project migrates data and reporting from a legacy estate to a modern platform built on Databricks and Microsoft Fabric. Quality engineering ensures that migrated data reconciles with the source, pipelines behave as specified, and reports produce consistent results.

• Design and implement data testing and quality assurance frameworks
• Build automated validation for data pipelines and data products
• Own source-to-target reconciliation between legacy systems and the new platform
• Build repeatable regression packs that run unattended
• Write unit tests that run automatically within CI/CD, with quality gates that can block a release
• Detect and handle schema drift and unexpected structural changes
• Validate reports, dashboards, and semantic models, including measure definitions, filter behaviour, and totals
• Apply AI-assisted techniques to test generation and coverage analysis, with human review

• 5+ Years of experience in quality engineering, including meaningful recent experience testing data or ETL processes rather than applications
• Strong Python and SQL skills used to build test automation, not only run it
• Experience with pipeline validation, including row counts, control totals, referential integrity, and business rule assertions
• Experience with source-to-target reconciliation using defined tolerances
• Experience with test automation within CI/CD, including gates that block a release
• Experience with Azure DevOps
• Experience with SQL Server
• English proficiency suitable for direct client conversations
• Будет плюсом: experience with Azure Databricks or Microsoft Fabric, data quality frameworks such as Great Expectations, dbt tests, Databricks Expectations, DLT quality rules, Soda, or Deequ, designing a test framework from scratch, testing semantic models and BI layers, working in a regulated financial services environment, application test automation alongside data testing using Selenium, C#, SpecFlow, or Playwright

• Условий в вакансии нет