Quality Automation Lead
Listed on 2026-04-14
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IT/Tech
Data Analyst, Data Engineer, Data Security, Data Warehousing
Introduction
Welcome to Gallagher - a global community of people who bring bold ideas, deep expertise, and a shared commitment to doing what’s right. We help clients navigate complexity with confidence by empowering businesses, communities, and individuals to thrive. At Gallagher, you’ll find more than a job; you’ll find a culture built on trust, driven by collaboration, and sustained by the belief that we’re better together.
Whether you join us in a client-facing role or as part of our brokerage division, our benefits and HR consulting division, or our corporate team, you’ll have the opportunity to grow your career, make an impact, and be part of something bigger. Experience a workplace where you’re encouraged to be yourself, supported to succeed, and inspired to keep learning. That’s what it means to live The Gallagher Way.
Job Description: Quality Automation Lead
Position Title: Quality Automation Lead
Department: Global Data Technology
Location: Remote
Reports To: Director of Quality Engineering
Arthur J. Gallagher is one of the world’s largest insurance brokerage and risk management companies, providing tailored insurance solutions and strategic organizational wellbeing initiatives. With over 50,000 employees globally, Gallagher is committed to innovation, client success, and delivering high-quality solutions across all aspects of the business.
Please note additional position details below:
- This is a Temp-To-Hire, W-2 position. We are not able to do 1099 or C2C.
- It is a fully remote role that will need to be based in the U.S.
- You must meet our U.S. Eligibility requirements for work authorization as noted under "Additional Information" at the bottom of the job description.
Position Summary
The Quality Automation Lead will serve as the primary architect of our enterprise data integrity and validation strategy. This role focuses on building automated, defense-in-depth testing solutions to ensure our insurance data remains accurate, consistent, and trusted across all BI and reporting platforms. The individual will develop scalable ETL and data-pipeline validation frameworks, lead complex data migration reconciliations, and drive UAT readiness to maintain a reliable, enterprise-wide single source of truth.
Self-motivated in handling multiple assignments across cross-functional teams.
- Lead QA Automation strategy and execution for ETL/DWH, data migration, and BI/reporting initiatives.
- Establish best practices for automated testing, data quality frameworks, and continuous validation processes.
- Mentor and guide QA engineers, ensuring high-quality deliverables and best practices.
- Work closely with Data Engineering, Product Owners, and Business stakeholders to define testing approaches and quality standards.
- Drive continuous improvement in QA processes, automation, and data validation frameworks.
- Design, implement, and maintain automated frameworks for validating ETL workflows, data lakes, and data warehouse processes.
- Validate data transformations, aggregations, business rules, and lineage across multi-layered architectures.
- Perform comprehensive data profiling, schema validation, and quality rule testing through automation.
- Ensure accuracy, completeness, and consistency across large-scale datasets and integrated systems.
- Integrate automated checks into orchestration tools such as Apache Airflow.
- Lead high-visibility data migration and system conversion validation initiatives.
- Define and execute reconciliation strategies across source and target systems to ensure zero-defect migration outcomes.
- Drive cross-functional resolution of data discrepancies and mapping issues.
- Validate end-to-end data flows supporting enterprise BI and reporting platforms.
- Perform report-to-data source validation for dashboards, operational reports, and analytical applications.
- Certify UAT readiness with structured data validation, defect management, and sign-off processes.
- Build and maintain automated testing frameworks for data pipelines,…
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