Software Engineering, Senior Data Engineer

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.

In conjunction with the Investment Data Management Office, the Senior Investment Data Engineer contributes to a strategic initiative to unify and harmonize investment data across the organization. This initiative supports enhanced investment decision making, risk management, analytics, and client reporting for a multi-asset investment platform by delivering consistent, timely, accurate, and user‑friendly data to investment professionals, risk teams, and clients.

Are you a hands‑on and detail‑oriented individual interested in financial instruments, investment data, and analytics? Do you enjoy solving complex data challenges and building scalable data solutions? Are you interested in working across a broad range of traditional and alternative asset classes while collaborating with business and technology teams in a fast‑paced environment?

The Investment Data Management Office is seeking a Senior Investment Data Engineer to support the implementation and maintenance of modern data engineering and analytics solutions. This role will contribute to the development, enhancement, and support of investment data platforms and pipelines while partnering closely with data architects, platform engineers, investment teams, and vendor partners. This position offers the opportunity to grow technical expertise and contribute meaningfully to the future of investment data capabilities

• Develop and maintain data models in dbt (Data Build Tool) within Snowflake, implementing business logic and adhering to established data standards and architectural principles.
• Contribute to dbt projects by building modular, scalable, and well‑documented transformation pipelines.
• Design, develop, and support scalable data pipelines and data warehouse solutions for reporting, analytics, and operational use cases.
• Participate in development activities and support implementation of solutions aligned with business and program objectives.
• Assist in improving data quality, resiliency, controls, monitoring, and operational efficiency across the investment data platform.
• Troubleshoot data and system issues, identify root causes, and support resolution efforts.
• Partner with senior engineers, platform leads, architects, and business stakeholders to support delivery of investment data solutions.
• Support integration of data ingestion, transformation, orchestration, and validation tools within the unified data platform.
• Contribute to enhancement of data engineering and analytical engineering capabilities across the platform.
• Provide operational and production support, including assistance during unexpected outages or incidents.

• Bachelor's degree in Computer Science or related discipline.
• 3-5+ years of experience designing, developing, and supporting data‑oriented applications and platforms.
• 2+ years of hands‑on experience with SQL and advanced SQL concepts.
• Experience developing and maintaining data models using dbt (Data Build Tool).
• Experience working with data integration (ETL/ELT), data warehousing, and analytics architectures, with understanding of data engineering best practices and design principles.
• Experience with Snowflake and/or other cloud-native databases is preferred.
• Development experience with cloud‑based PaaS platforms such as Microsoft Azure, Google GCP, or Amazon AWS.
• Familiarity with Agile SDLC, Dev Ops practices, CI/CD processes, and cloud technologies.
• Understanding of modern data architecture concepts such as unified data management, data mesh, event‑driven architecture, real‑time data flows, and data virtualization is a plus.
• Exposure to financial services, investment management, or investment data domains is preferred.
• Strong analytical,…