Full Description

About Arthur J. Gallagher 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. Role Summary The QE Lead – Data Operations is a dual-role position focused on ensuring production data reliability and embedding quality engineering into enterprise data platform development. Responsibilities include end-to-end data quality validation, operational testing, defect prevention, and test automation across maintenance, incident resolution, enhancements, and new development. The role collaborates with Data Engineering, Data Ops, BI, and Business SMEs to integrate quality controls into daily operations and promote scalable, shift-left testing practices.

Key Responsibilities Act as the QE contact for Data Ops and support teams. Mentor QA engineers to ensure quality deliverables and best practices. Collaborate with Data Engineering, DevOps, BI teams, and business SMEs for DataOps activities. Manage defect triage, root cause analysis, and data issue resolution. Establish processes for data quality monitoring, validation, and improvement. Communicate risks, quality status, and release readiness to leadership. Support L2/L3 production incidents, including data defects, report mismatches, and pipeline failures. Perform root cause analysis (RCA) and collaborate on permanent fixes. Validate hotfixes, break-fix changes, and data. Ensure regression testing during support releases. Lead QA for new data pipelines, transformations, schema changes, and reporting enhancements. Review requirements, mappings, and designs for testability. Define test strategies for incremental and large-scale data initiatives. Validate end-to-end data flow from source systems to BI consumption. Promote shift-left testing in development cycles. Define and enforce data quality rules (accuracy, completeness, timeliness, consistency). Validate ETL/ELT pipelines, transformations, and business rules. Ensure validation of critical financial and regulatory reports. Establish data reconciliation and variance analysis processes. Write and review complex SQL scripts for validation and reconciliation. Support pipeline automation using tools like DBT, Dataiku, and Airflow. Create reusable frameworks for data quality checks and automated testing. Integrate automated tests into CI/CD pipelines. Use Python and data testing tools to reduce manual effort. Define quality gates for deployments and data releases. Set SLAs, KPIs, and quality metrics for Data Ops. Ensure compliance with data governance, audit, and regulatory standards. Maintain test documentation, runbooks, and operational dashboards.

Qualifications Education A bachelor’s degree in computer science, Software Engineering, Information Systems, or a related field is required, or equivalent professional experience.