At MFS, you will find a culture that supports you in doing what you do best. Our employees work together to reach better outcomes, favoring the strongest idea over the strongest individual. We put people first and demonstrate care and compassion for our community and each other. Because what we do matters – to us as valued professionals and to the millions of people and institutions who rely on us to help them build more secure and prosperous futures.
THE ROLE
In partnership with Enterprise Data Management (EDM), the Lead Data QA Engineer will play a key role in a long-term strategic initiative to unify and harmonize investment data across MFS. This initiative supports enhanced investment decision-making, risk management, and client reporting for our multi-asset platform by delivering consistent, timely, accurate, and reliable data to investment professionals, risk teams, and clients.
We are seeking a hands-on, detail-oriented quality engineering leader with experience in financial instruments, investment data, and analytics. The ideal candidate brings a strong engineering mindset to data quality and testing, with a focus on building scalable automation frameworks rather than relying on manual validation. This individual will help define and implement QA strategies across traditional and alternative asset classes while developing innovative and flexible solutions to evolving business and technology needs.
The Enterprise Data Management team is seeking a Lead Data QA Engineer to lead the design, implementation, and continuous improvement of automated data quality and validation solutions for our unified data platform. This role will work closely with data engineers, platform engineers, product owners, investment professionals, and external data providers in an Agile, collaborative environment. The position offers an opportunity to establish quality engineering best practices and help shape the future of investment data at MFS.
WHAT YOU WILL DO
Lead the design, development, and implementation of automated data testing and validation frameworks to ensure the quality, accuracy, completeness, and reliability of data within the unified data platform.
Define and drive the overall Data QA strategy, including test planning, automation, regression testing, integration testing, reconciliation, and production data validation.
Develop reusable, scalable automated testing capabilities for data pipelines, transformations, business rules, APIs, databases, and data products.
Establish comprehensive test coverage across data sourced from internal systems, external vendors, and third-party providers.
Validate complex investment data, including financial instruments, security master/reference data, transactions, positions, pricing, and related datasets.
Develop automated controls for data completeness, accuracy, consistency, timeliness, uniqueness, integrity, and reconciliation.
Partner with data engineers and business stakeholders to translate business and data requirements into test scenarios, acceptance criteria, and automated test cases.
Lead testing throughout the SDLC, including functional, integration, regression, end-to-end, performance, and production validation.
Integrate automated data quality testing into CI/CD pipelines to enable early detection of defects and support reliable software and data releases.
Implement monitoring, alerting, data observability, and quality metrics to proactively identify data issues and emerging quality trends.
Lead root-cause analysis of complex data defects and pipeline failures, working with engineering and business teams through resolution.
Define Data QA standards, best practices, controls, and quality metrics while mentoring engineers and promoting a strong quality engineering culture.
Drive continuous improvement in data quality, test automation, platform resiliency, operational controls, and engineering efficiency.
Provide technical leadership during production incidents and support resolution of…