Databricks QA Engineer

Responsibilities

You will own the test strategy and release sign-off for enterprise data platforms, ensuring data quality through automated suites. This involves building and maintaining validation frameworks in Python and PySpark while partnering with engineers to gate deployments.

Requirements

Candidates must have 5+ years of experience in QA automation with a strong background in data engineering and pipeline testing. Expert-level SQL skills and hands-on experience with Databricks or similar platforms are required.

Skills

• Databricks
• Python
• PySpark
• SQL
• Data Engineering
• QA Automation
• Delta Lake
• Medallion Architecture
• CI/CD
• Test Strategy
• Data Quality
• ELT Workflows
• Unity Catalog
• Lakeflow
• Azure DevOps
• pytest

About this role

About V4C
V4C is a pure-play Databricks partner, and in under two years we have earned 850+ Databricks certifications across 325+ customers and been named Databricks' 2026 Partner of the Year (C&SI Emerging).
As a Databricks QA Engineer on our data engineering team you will own the quality gate for enterprise data platforms, working directly with customers. You will work on live enterprise data platforms alongside one of the most heavily Databricks-certified delivery teams in the market.

The Role
This is a QA role for people who understand data engineering, not general software QA pointed at a data project. You will validate Lakehouse pipelines, which means reasoning about row counts, schema drift, late-arriving data, and silent transformation errors no UI will ever surface. Our approach is automation-first: suites in Python and PySpark, running in CI, blocking promotion when they fail. You own release sign-off, which makes you the last person between a customer and bad data in production.
Responsibilities
Own test strategy and release sign-off on engagements, including documented test plans covering scope, risk, and coverage
Build and maintain automated test suites in Python and PySpark (pytest), integrated into CI pipelines
Validate data pipelines end to end: ingestion, Medallion layer transformations, Delta Lake tables, and ELT workflows
Implement data quality rules as code, including Lakeflow expectations and reusable validation frameworks
Run regression validation after code changes and maintain CI quality gates that block merge or promotion on failure
Partner with engineers to identify automation candidates and raise the coverage bar across the practice
5+ years in QA automation or test engineering, including meaningful time testing data pipelines rather than applications alone
A data engineering background: you can read a pipeline, reason about a transformation, and write the SQL to verify it
Expert-level SQL
Experience with Medallion architecture, Delta Lake, and ELT-based data platforms
Experience building automated suites that run in CI and gate deployment
Demonstrable hands-on experience working on Databricks or similar platforms
Preferences
Healthcare or life sciences experience preferred but not required
Existing Databricks certification(s)
Hands-on experience with Unity Catalog, Lakeflow pipelines, or Databricks Asset Bundles
Experience with Azure DevOps pipelines, or equivalent CI tooling (GitHub Actions, GitLab CI)
Familiarity with contract-based data validation standards such as ODCS
Experience using LLM agents or MCP-connected tooling to run validations, triage failures, or draft defect reports
Consulting or client-facing background
Pay Rate: $70 – $80 per hour (1099 independent contractor).
This is a contract engagement rather than a salaried employee role. Where a rate lands within the range depends on depth of data-pipeline testing experience and automation capability. Open to contractors anywhere in the United States.

About V4C.ai

IT Services and IT Consulting · 501-1,000 employees · headquartered in Scottsdale, AZ