Vice President, Data Architect, Data Service and Governance

Career Opportunities with Income Research Management

A great place to work.

Careers At Income Research Management

Share with friends or Subscribe!

Are you ready for new challenges and new opportunities?

Join our team!

Current job opportunities are posted here as they become available.

Subscribe to our RSS feeds to receive instant updates as new positions become available.

Income Research + Management is a Boston-based, privately owned, fixed income asset management firm. IR+M delivers strong performance and consistent results through a rigorous, bottom-up security selection process and strives to provide best-in-class client service to our 800+ institutional and private wealth clients.

Founded in 1987 and located in the heart of Boston’s financial district, IR+M employs 200 full time professionals and currently manages $135+ Billion in assets. We offer industry-leading benefits, as well as a challenging, collegial, and rewarding workplace with high levels of employee engagement.

Open Position: VP, Data Architect, Data Service and Governance

The VP, Data Architect role is a hands-on technical leader within the Enterprise Data Service and Governance Team. This individual will design, build, and support modern data architectures that span on-premises and Azure cloud environments. This role requires deep expertise in investment management data domains. Strong hands-on data architecture skills, and the ability to translate complex investment and operational requirements into robust data models and integration patterns.

The right candidate brings deep expertise across the full data stack — from logical to physical modeling, data warehouse to data lake, analytics to reporting — and knows how to match the right platform to support the firm’s investment management and operation functions. This is a thinker and a doer role: design, plan, implement, and support.

• Perform hands-on development alongside architecture and design work
• Conduct code and design reviews, and suggest and validate unit test cases
• Contribute to the evolving data platform by evaluating, prototyping, and adopting new technologies
• Collaborate with the Head of Data Services and Governance and Business/Data Analysts to define project design and influence requirements
• Provide technical direction, lead design discussions and resolve conflicts
• Contribute to the short- and long-term data technology roadmap
• Participating in on-call rotation for operational support
• Triage and troubleshoot data discrepancies, reporting breaks, and system issues
• Work with stakeholders to resolve root causes of operational and data errors
• Support testing and validation of system enhancements before release
• Assist with User Acceptance Testing (UAT) for operational system upgrades or enhancements
• Communicate clearly with business users on issue status and resolution timelines

• Cloud-native ELT/ETL pipeline design and implementation
• Datamart/lakehouse architecture, multi-layer ELT design
• Dimensional modeling: star schemas, facts, dimensions, and analytical data structures
• Data models for reporting, analytics, and downstream consumption layers
• CI/CD pipelines and automated deployment for data engineering workloads
• Git-based source control, branching, pull requests, and release workflows via Azure Dev Ops and/or Git Hub
• Azure Key Vault, managed identities, and RBAC for secure data access

• Strong understanding of available data platforms (on-prem and cloud) with ability to right-fit technology to business need
• Experience with 3rd party Enterprise Data Management systems (Markit EDM, Asset Control, Golden Source, or similar)
• Deep knowledge of buy-side asset management data flows is a strong plus
• Ability to work in a fast-paced, agile environment managing multiple priorities simultaneously
• Strong written and verbal communication skills
• AI-aware mindset with the ability to identify opportunities to embed intelligent automation within data pipelines and architecture
• Experience applying AI to data quality use cases such as anomaly detection, exception handling, and automated…