Senior Manager - Data
Location: Boston, MA (4 days onsite required)
Work Authorization: Must be authorized to work in the U.S. without sponsorship
About the Firm
We are partnered with a global asset management organization managing trillions of dollars in assets for institutional and retail investors worldwide. Their investment platform spans public and private markets, active and passive strategies, and supports a broad range of portfolio solutions for clients across regions and asset classes.
The Team
You will join the Investments Technology organization, supporting a Solutions‑focused investment group. The team builds and operates mission‑critical platforms used by portfolio managers, quantitative researchers, analysts, and client‑facing investment teams to construct portfolios, run analytics, assess risk, and deliver investment models at scale.
Role Summary
We are seeking a Senior Engineering Manager to lead a core portfolio analytics and model delivery platform used across the Solutions investment organization.
This role is not purely managerial. It requires:
- Deep, hands‑on experience in financial services, specifically portfolio analytics and risk data
- Daily, independent work with Python, SQL, and ETL pipelines
- Proven people leadership, with 5+ years managing engineering teams
- Experience with implementing AI tools (e.g., GitHub Copilot, Claude, ChatGPT, AWS Bedrock) for coding, tests, documentation, and prototyping
You will own system architecture and technical direction while leading a distributed engineering team across the U.S. and offshore. The ideal candidate combines strong engineering judgment, financial data expertise, and a product mindset to deliver reliable, secure, and cost‑efficient investment technology.
Key Responsibilities
- Own the technical roadmap for a portfolio analytics and model delivery platform
- Set architectural direction aligned to investment workflows in asset allocation, custom portfolios, and quantitative strategies
- Design systems that handle large‑scale financial data, analytics, and downstream model distribution
- Remain deeply hands‑on: design, review, and debug Python services, SQL queries, and ETL workflows
- Lead by example on code quality, performance, testing, and production readiness
- Independently work through complex data issues involving portfolio, benchmark, factor, and risk datasets
- Lead, mentor, and develop a team of experienced engineers
- Institutionalize disciplined daily use of AI tools for coding, testing, documentation, and prototyping
- Establish review standards to ensure AI‑generated code meets security, performance, and maintainability requirements
- Apply AI where it materially improves developer velocity without compromising reliability or compliance
- Raise the bar on testing, CI/CD, rollout/rollback strategies, SLOs, and incident postmortem
- Partner with SRE on observability, automation, and operational consistency
- Drive cloud cost discipline (FinOps) through right‑sizing, scheduling, and cost visibility
- Master's degree in Computer Science, Statistics, or a related quantitative field
- Investment or risk credentials (CFA, CAIA, FRM, CIPM) preferred but not required
FAQs
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