Lead Quality Engineer - Automation & Manual (Data Domain)

Introduction At Gallagher, we help clients face risk with confidence because we believe that when businesses are protected, they’re free to grow, lead, and innovate. You’ll be backed by our digital ecosystem: a client-centric suite of consulting tools making it easier for you to meet your clients where they want to be met. Advanced data and analytics providing a comprehensive overview of the risk landscape is at your fingertips. Here, you’re not just improving clients' risk profiles, you’re building trust. You’ll find a culture grounded in teamwork, guided by integrity, and fueled by a shared commitment to do the right thing. We value curiosity, celebrate new ideas, and empower you to take ownership of your career while making a meaningful impact for the businesses we serve. If you’re ready to bring your unique perspective to a place where your work truly matters; think of Gallagher. Overview At Gallagher, we are united by a commitment to excellence and innovation. As a Quality Engineer Lead – Data Operations, you’ll play a pivotal role in ensuring the quality and reliability of our enterprise data platforms. Based in Sri Lanka, this dual-function role offers the opportunity to lead a team, support business-critical data operations, and embed quality engineering practices into ongoing development. This is your chance to work with a global team, contribute to cutting-edge data solutions, and make a meaningful impact on our organisation’s success. How you'll make an impact In this role, you’ll lead quality engineering efforts for both production support and development activities. You’ll ensure business-critical data remains reliable in production while embedding quality practices into ongoing development. Day-to-day, you’ll: Act as the primary Quality Engineering (QE) contact for Data Operations and support teams. Mentor and guide QA engineers, ensuring high-quality deliverables and best practices. Oversee defect triage, root cause analysis, and data issue resolution. Lead testing for new data pipelines, transformations, schema changes, and reporting enhancements. Define and enforce data quality rules, validate ETL/ELT pipelines, and ensure critical reports are accurate. Write and review SQL scripts for validation and reconciliation. Contribute to pipeline automation using tools like DBT, Dataiku, and Airflow. Establish reusable frameworks for data quality checks and automated testing. Define quality gates for deployments and data releases while maintaining test documentation and dashboards. About you Here’s what you’ll bring to the role: A degree in Computer Science, Software Engineering, Information Systems, or equivalent experience. 6-8+ years of experience with a strong focus on data-centric testing, including ETL processes, data warehouses, and data lakes. 3+ yeares in a QE Lead or SME role for data platforms Proficiency in SQL and familiarity with data validation tools like QuerySurge, Selenium, or Python-based frameworks. Knowledge of cloud data platforms (e.g., Snowflake, Azure Synapse) and BI tools (e.g., Power BI, Tableau). Strong collaboration, problem-solving, and communication skills. Preferred: QA certifications (e.g., ISTQB) and familiarity with AI/ML-based testing tools. #LI-Hybrid

Here’s what you’ll bring to the role: A degree in Computer Science, Software Engineering, Information Systems, or equivalent experience. 6-8+ years of experience with a strong focus on data-centric testing, including ETL processes, data warehouses, and data lakes. 3+ yeares in a QE Lead or SME role for data platforms Proficiency in SQL and familiarity with data validation tools like QuerySurge, Selenium, or Python-based frameworks. Knowledge of cloud data platforms (e.g., Snowflake, Azure Synapse) and BI tools (e.g., Power BI, Tableau). Strong collaboration, problem-solving, and communication skills. Preferred: QA certifications (e.g., ISTQB) and familiarity with AI/ML-based testing tools. #LI-Hybrid

In this role, you’ll lead quality engineering efforts for both production support and development activities. You’ll ensure business-critical data remains reliable in production while embedding quality practices into ongoing development. Day-to-day, you’ll: Act as the primary Quality Engineering (QE) contact for Data Operations and support teams. Mentor and guide QA engineers, ensuring high-quality deliverables and best practices. Oversee defect triage, root cause analysis, and data issue resolution. Lead testing for new data pipelines, transformations, schema changes, and reporting enhancements. Define and enforce data quality rules, validate ETL/ELT pipelines, and ensure critical reports are accurate. Write and review SQL scripts for validation and reconciliation. Contribute to pipeline automation using tools like DBT, Dataiku, and Airflow. Establish reusable frameworks for data quality checks and automated testing. Define quality gates for deployments and data releases while maintaining test documentation and dashboards.