Job Summary
The QA Lead - Enterprise Data Platform is the hands-on technical authority for the offshore QA team. This role owns the technical direction for testing across Snowflake, Databricks, and Azure Data Factory, establishes standards for test design and automation, and serves as the primary technical escalation point for complex data quality issues, schema changes, and root cause analysis. The QA Lead partners closely with the Onshore Team Lead on prioritization, delivery, and stakeholder communication.
Key Responsibilities
• Define the technical testing strategy across Snowflake, Databricks, and Azure Data Factory, including data quality frameworks, validation patterns, and test data management approaches.
• Establish and enforce QA standards, best practices, and reusable accelerators, including test harnesses, validation libraries, and SQL templates.
• Provide technical oversight on test design for functional and regression testing, schema evolution, source-to-target validation, reconciliation, and backfill/reprocessing verification.
• Lead complex root cause analysis for data quality incidents, including impact assessment, and coordinate resolution with data engineering teams.
• Own the automation roadmap and review and approve Python-based test scripts, SQL-based validations, and DevOps pipeline integrations using Azure DevOps.
• Mentor Senior QA Engineers, Automation QA Engineers, and QA Engineers/Testers and perform code and test case reviews.
• Partner with the Onshore Team Lead on sprint planning, capacity, risk identification, and stakeholder communication.
• Monitor data quality KPIs, dashboards, and alerts and drive continuous improvement initiatives based on trends and incident patterns.
• Support schema changes, backward compatibility analysis, and impact assessment for upstream and downstream consumers.
Required Qualifications
• 5-8 years of QA/data testing experience, with at least 2 years in a technical lead or senior individual contributor capacity.
• Strong hands-on experience testing data pipelines on Snowflake, Databricks (PySpark/SQL), and Azure Data Factory.
• Advanced SQL skills, including complex joins, window functions, reconciliation queries, and performance-aware query writing.
• Proficiency in Python for test automation, including frameworks such as pytest and libraries such as pandas, Great Expectations, or equivalent.
• Experience with Azure DevOps or equivalent CI/CD tools for automated test execution, pipeline integration, and reporting.
• Solid understanding of data warehousing concepts, ELT/ETL patterns, schema evolution, slowly changing dimensions, and data reconciliation.
• Demonstrated experience leading root cause analysis (RCA) for data incidents and communicating findings to technical and business stakeholders.
• Strong written and verbal communication skills with the ability to work across onshore and offshore time zones.
Preferred Qualifications
• Experience with data quality tools such as Great Expectations, Soda, Monte Carlo, or Collibra DQ.
• Exposure to data governance, lineage, and metadata management tooling.
• Experience with Git-based workflows, code reviews, and trunk-based development practices.
Certifications
• Azure certifications such as DP-203 or AZ-400.
• Snowflake or Databricks certifications.
