Data QA Lead

About the role

The Data QA Lead acts as the QA owner for one delivery team, working in a full-time capacity. The role covers the entire quality lifecycle, from acceptance criteria and test design to defect tracking, release sign-off and quality reporting.

Key responsibilities

• Take part in backlog refinement, sprint planning, daily scrum, reviews and retrospectives.
• Apply a shift-left, test-driven approach: agree acceptance criteria and test design with developers and business analysts before development starts.
• Plan and estimate QA effort per work item, commit QA capacity at sprint planning and manage it through the sprint.
• Lead and mentor the QA engineers on the team, reviewing their test designs, queries and defect reports.
• Design and run data validation, source-to-target, integration, regression and post-production checks across SSIS, dbt, Fivetran, Python ingestion and Databricks pipelines.
• Confirm accuracy, latency, performance and security gates are met before each environment promotion and give QA sign-off for release.
• Log, triage and track defects in Azure DevOps, drive resolution with developers and communicate impact to the business analyst and product owner.
• Report team-level quality metrics such as first-pass quality, defect escape rate, automated regression coverage and QA cycle time.
• Support knowledge transfer and shadow coverage during transitions, maintain test assets and runbooks, and lead root cause analysis for post-production defects.
• Use AI/LLM tools (e.g. Claude, Copilot) to speed up test case generation, synthetic data and query review, validating AI-assisted output before use.
• Communicate QA status, risks and release readiness to the Practice Lead and US stakeholders.