Hot Requirement | Databricks QA Engineer | Remote | C2C

• Test Case Development — Create clear, repeatable test cases covering functional, negative, edge, and regression scenarios.
• Manual Execution — Perform UI, functional, integration, and exploratory testing across environments.
• Bug Identification & Reporting — Log defects with detailed steps, environment info, severity, priority, and supporting evidence.
• Regression Testing — Re-validate existing functionality after code changes to ensure stability.

• Automation Awareness — Understand automation coverage, identify automation candidates, and collaborate with automation engineers.
• Data Validation — Verify backend data, database entries, and system states for accuracy and consistency.
• Data Engineering Background

• Deep understanding of Medallion architecture
• Experienced in testing data ingestion, 3-layer data lake, Delta Lake, and ELT-based applications
• Extensive test automation experience using Python and PySpark
• Expert-level knowledge of SQL

• Test Plans — Documented scope, strategy, risks, and coverage.
• Test Cases & Checklists — Structured, traceable test documentation.
• Defect Reports — Clear, actionable bug tickets with supporting evidence.
• Release Sign-off — Final validation ensuring readiness for deployment.

• Modern QA practices: The client needs a modernized QA approach built on contract-based validation standards (e.g. ODCS), automation and AI-assisted testing; we are sourcing QA profiles with this expertise.
• Databricks data engineering background: hands-on with Unity Catalog, Lakeflow pipelines and Asset Bundles, beyond test execution.
• Automation-first: automated suites in Python/PySpark (pytest); manual testing only for exploratory testing and UAT.
• CI quality gates: tests run in Azure DevOps pipelines and block merge or promotion when they fail.
• Data quality as code: quality rules (Lakeflow expectations / DQX) that extend the existing validation framework.
• AI/MCP-enabled QA: hands-on use of LLM agents connected via MCP to Azure DevOps and Databricks to run validations, triage failures and draft defects.
• PHI-safe practices: synthetic or masked test data only when working with LLMs.

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