Job Description & Details
This is a heavy-hitting leadership role bridging the gap between business stakeholders and technical data engineering teams in the asset management space. You will not just be managing spreadsheets; you will be architecting the foundational data infrastructure that drives investment workflows and reference data governance.
What You'll Actually Be Doing
Your day-to-day will revolve around translating ambiguous business needs from portfolio managers and clients into concrete technical requirements for your dev teams. You'll spend a lot of time auditing reference data standards, tightening data-quality controls, and figuring out why vendor feeds from Bloomberg or ICE are breaking reconciliation pipelines. You will also act as the technical anchor, guiding engineers on exception-handling workflows and automation while mentoring junior analysts to elevate the overall delivery standard.
The Core Tech Stack
You need deep, battle-tested experience with Enterprise Data Management (EDM) or Security Master platforms, alongside heavy exposure to front-to-back office systems like Aladdin, Charles River, or SimCorp. SQL and advanced Excel are non-negotiable for doing heavy lifting on data mapping and root-cause analysis. Familiarity with vendor feeds like LSEG, S&P, and Bloomberg is crucial, while bonus points go to anyone bringing Python scripts or private markets/ESG data ingestion to the table.
Interview Expectations
Expect the interviewers to probe deeply into your architectural decisions regarding reference data lineage and exception management. They will likely ask you to explain a time when a critical market data feed failed during peak trading hours and how you resolved the root cause under pressure. The hiring manager isn't just looking for a technical answer; they want to see how you communicate failure scenarios to non-technical stakeholders and enforce governance without slowing down the business.
Application Advice
When tailoring your resume for this role, make sure to highlight specific metrics around data governance projects you've led, such as the volume of securities managed or the percentage reduction in data reconciliation errors. Explicitly list out your experience with vendor datasets like Bloomberg or LSEG and platforms like Aladdin or SimCorp so your profile immediately trips the ATS keywords for enterprise data management.