Lead Data Quality Assurance Engineer

MFS Investment Management • Boston, MA

4d • $88,000 - $126,000 • Hybrid

About The Position

The Lead Data Quality Assurance Engineer will partner with Enterprise Data Management on a long-term strategic initiative to unify and harmonize investment data across MFS. The initiative aims to provide consistent, timely, accurate, and reliable data for investment decision-making, risk management, and client reporting across the multi-asset platform.

This hands-on technical leadership role designs, implements, and continually improves automated data quality and validation solutions for a unified data platform. The engineer will define QA strategies for traditional and alternative asset classes, develop scalable automation rather than relying on manual validation, and collaborate with data and platform engineers, product owners, investment professionals, and external data providers in an Agile environment. The role also establishes quality engineering practices and helps shape MFS’s investment data platform.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline.
• At least 5 years of experience in data quality engineering, data testing, QA automation, data engineering, or related roles, including experience leading complex data quality initiatives.
• Strong experience designing and building automated testing frameworks for large-scale data platforms, pipelines, and transformations.
• Advanced SQL skills and ability to validate complex datasets, transformations, reconciliation rules, and business logic.
• Strong programming and test automation experience using Python or comparable languages and frameworks.
• Experience testing ETL/ELT pipelines, data warehouses, APIs, databases, and cloud-based data platforms.
• Understanding of data quality dimensions, data observability, metadata, lineage, data governance, and data catalog concepts.
• Experience integrating automated testing into CI/CD and DevOps processes.
• Strong understanding of Agile SDLC practices and experience partnering with engineering, product, and business teams.
• Strong analytical and troubleshooting skills, including identifying root causes across complex data pipelines and distributed systems.
• Demonstrated technical leadership, including establishing QA standards, providing technical guidance, mentoring team members, and influencing engineering practices across teams.
• Strong written and verbal communication and ability to collaborate with technical teams, investment professionals, business stakeholders, and external vendors.
• Must be currently authorized to work in the United States on a full-time basis; the position is not eligible for sponsorship.

Nice To Haves

• Experience with Snowflake and cloud-native data technologies is strongly preferred.
• Experience with financial services and investment data—including financial instruments, securities, transactions, positions, pricing, or reference data—is highly desirable.

Responsibilities

• Lead the design, development, and implementation of automated data testing and validation frameworks for data quality, accuracy, completeness, and reliability.
• Define and drive Data QA strategy, including test planning, automation, regression and integration testing, reconciliation, and production data validation.
• Develop reusable, scalable automated tests for data pipelines, transformations, business rules, APIs, databases, and data products.
• Establish test coverage for data sourced from internal systems, external vendors, and third-party providers.
• Validate investment data, including financial instruments, security master and reference data, transactions, positions, pricing, and related datasets.
• Develop automated controls for data completeness, accuracy, consistency, timeliness, uniqueness, integrity, and reconciliation.
• Translate business and data requirements into test scenarios, acceptance criteria, and automated test cases in partnership with data engineers and business stakeholders.
• Lead functional, integration, regression, end-to-end, performance, and production validation throughout the SDLC.
• Integrate automated data quality testing into CI/CD pipelines to detect defects early and support reliable releases.
• Implement monitoring, alerting, data observability, and quality metrics to identify data issues and quality trends proactively.
• Lead root-cause analysis of complex data defects and pipeline failures, coordinating resolution with engineering and business teams.
• Define Data QA standards, best practices, controls, and quality metrics; mentor engineers and promote a 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 help resolve critical data quality issues and unexpected outages.

Benefits

• Competitive incentive bonus based on individual and company performance.
• MFS contributes an amount equal to 15% of base salary to the employee’s retirement account, separate from the company-sponsored 401(k).
• Education assistance: $100 monthly contribution directly to a loan provider, up to a $10,000 lifetime maximum.
• Tuition reimbursement up to $8,000 annually.
• Access to discounted tutors and college coaches.
• Generous time off and fully paid leaves, including 20 weeks for maternity leave and 12 weeks for parental and caregiver leaves.
• Choice of medical and dental plans and an employer contribution to a Health Savings Account.
• Tax-deferred commuter benefits and flexible spending accounts for medical and dependent care.
• Wellness programs, including wellness webinars, an employee assistance program focused on mental health, and a subsidized fitness benefit through Wellhub.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior