Senior Data Operations Analyst

Allocate transforms private market investing by enabling RIAs and family offices to discover, model, and manage their private market exposure. The platform provides curated fund and co-investment opportunities with institutional-grade infrastructure across venture capital, private equity, private credit, and other private asset classes.

The Opportunity

Private market data is messy. It arrives as unstructured documents from hundreds of managers and fund administrators in no consistent format, and it spans both the pre-investment picture — fund terms, manager and vehicle characteristics, track record — and post-investment reporting: capital account statements, capital calls, distributions, financial statements and tax documents.

This role owns data as a system across all of it, not a single dataset or a single product line: the fund and entity master, the checks that catch problems before clients do, the reconciliation that proves the numbers tie, and the analysis that explains where data is wrong and why. It is a broad remit

The work is equal parts investigation and design. You will spend as much time working out why something broke as you will building the thing that stops it breaking again.

What You'll Own

• Data as a system, across every product line. A consistent, governed view of funds, entities and positions that holds up wherever it is consumed — operated vehicles, administered funds, and client-held positions alike.

• Fund and entity data quality — the security master. Fund and manager records, entity resolution and duplicates, fund properties and characteristics.

• Data quality and completeness. Design and run the checks that validate data before it reaches clients, and the exception workflows behind them.

• Reconciliation. Reconcile data across our systems and sources, and explain the differences clearly.

• Root-cause analysis. Trace a wrong number back to its source, size the problem, and specify the fix.

• Extraction quality. Expand the data we capture and evaluate how well our tooling captures it.

• Documentation. Definitions, assumptions and edge cases, written down and kept current.

Must Have Experience

• 3+ years in data operations, reference or master data, fund operations, or financial operations

• Basic SQL

• Hands-on data reconciliation experience

• Experience designing or operating data quality checks, validation rules or exception workflows

• A process improvement mindset — you would rather fix the cause than clear the queue faster

• Comfortable in a startup environment: ambiguity, shifting priorities, and limited existing process

• Detail-oriented, with strong, consistent judgment under SLAs

• Comfort with data and modern software, including AI-assisted tools

Nice To Haves

• Proficient SQL

• Python for data analysis

• Security master or reference data management experience (instrument, entity or fund master)

• Entity matching or entity resolution — canonical entities, deduplication and merge review

• Data modeling — dimensional modeling, dbt, or data warehouse concepts

• Private markets experience, particularly fluency with capital account statements, capital calls, distributions, schedules of investments and K-1s

• Prompt engineering, or evaluating model extraction output against golden datasets

• BI tooling — Metabase, Looker, Tableau or similar

Education

• Bachelor's degree, or equivalent practical experience

Compensation & Benefits

• Salary: $95,000–$135,000 base, plus potential discretionary bonus and equity.

• Benefits: Medical, dental, vision, 401(k), and responsible vacation time

Values & Culture

Allocate prioritizes world-class client experiences, welcomes unconventional thinking, emphasizes continuous learning, operates on meritocracy, encourages civil discourse, and embraces technological innovation and automation.