DBT Investment 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.

THE ROLE

In conjunction with the Investment Data Management Office, the Lead Data Engineer contributes to a long-term strategic initiative to unify and harmonize our investment data. This initiative enables enhanced investment decision making, risk management and client reporting for our multi-asset platform by delivering consistent, timely, accurate and user-friendly data to investors, risk teams and clients.

Are you a hands-on and detailed-oriented individual working on the cutting edge of financial instruments, investment data, and analytics? Are you interested in investment data strategies across a wide variety of traditional and alternative asset classes? Are you a thinker who enjoys devising innovative and flexible business solutions to meet emerging business needs?

The MFS Investment Data Management Office is actively searching for a Lead Data Engineer to implement data engineering and analytics solutions . Primary responsibilities include full implementation and maintenance of data ingestion, data maintenance, data validation and data delivery of investment data. We are looking for someone who thrives in an agile, collaborative, team-based environment, working closely with technology peers across MFS, investment professionals and key vendor partners.

This position offers the opportunity to shape the future of investment data at MFS.

WHAT YOU WILL DO

• Develop and maintain data models in dbt (Data Build Tool) within Snowflake, implementing business logic and ensuring alignment with existing architecture and data standards.

• Manage and contribute to dbt projects, ensuring code quality, proper documentation, and alignment with modular, scalable design patterns.

• Design, build, and administer scalable data pipelines and a robust data warehouse to support reporting, analytics, and operational use cases.

• Lead and participate in all development activities, develop and implement solutions to meet business requirements that align with program strategic objectives

• Drive continuous improvement of data quality, resiliency, control, efficiency, and monitoring

• Troubleshooting complex system interactions to find the root cause to problems

• Partner with platform lead to design, develop, implement and deploy new software components to investment data platform

• Partner with data architect to evaluate and finalize the unified data model

• Partner with integration architect to upgrade and integrate data ingestion and data delivery tools with the unified data platform

• Upgrade and integrate transformation tool, data validation tool and orchestration tools with the unified data platform to implement data engineering, analytical engineering and data maintenance capabilities.

• Provide support during unexpected outages

WHAT WE ARE LOOKING FOR

• Bachelor’s degree in Computer Science or related disciplines.

• 5-6+ years of experience in design, development and building data oriented complex applications.

• Minimum of 2-4 years of hands-on progressive experience from SQL to Advanced SQL.

• Experience in developing and maintaining data models in dbt (Data Build Tool)

• Experience working in data integration (ETL/ELT), data warehouse, data analytics architecture and sound understanding of design principles
  . Knowledge of and experience with Snowflake and other cloud native databases is highly preferred.

• Development Experience in Cloud based PAAS platforms like Microsoft Azure, Google GCP or Amazon AWS

• Deep understanding of Agile SDLC, Dev Ops and Cloud technologies required, in addition to exposure to multiple, diverse technologies, platforms, and processing environments.

• Knowledge about various architectures, patterns such as unified data management architecture (UDM), data mesh architecture, event-driven…