Job Description

COMPANY: Canoe Intelligence

WEBSITE: https://canoeintelligence.com/

TITLE: Data Operations Manager, Asset Data

LOCATION: Jacksonville, NYC, London Preferred or Remote

SALARY: $145,000 to $165,000 base salary plus bonus and equity

The Role: Canoe is expanding its asset data capability — cost basis, valuations, operating metrics, portfolio company financials — and we need a senior practitioner who can simultaneously own the team’s daily execution and define the logic that drives our extraction models forward.

Most private markets data roles ask you to either manage the work or do the work. This one expects excellence at both. The Data Operations Manager, Asset Data is the senior practitioner who sets the quality standard by example, builds the extraction logic that drives our AI forward, and develops the team that executes it every day.

What You’ll Do:

Lead the Team

• Own the day-to-day execution of the asset data team and hold the quality bar at every level

• Develop analysts through hands-on coaching, markup reviews, and direct feedback on domain knowledge gaps

• Set standards and enforce them — you are accountable for every data point your team produces

• Build and maintain GP-level and asset-class-specific playbooks that enable consistent, scalable processing across the team

Define the Logic

• Design comprehensive extraction rules for complex asset-level documents: portfolio company financials, operating metric reports, valuation statements, MOIC/IRR calculations, and multi-asset-class structures

• Codify financial concepts — valuation methodology nuances, cost basis adjustments, operating KPI definitions — into systematic rules that Product and Engineering can implement

• Research emerging reporting formats and asset class variations proactively; define the schema before edge cases hit production

• Serve as the final escalation point for asset data integrity — when automated logic encounters a novel structure, you resolve it and document the rule

Train the AI

• Personally analyze and annotate complex proprietary documents to establish ground truth for Canoe’s extraction models — your markup is the gold standard

• Provide direct input into AI model training by identifying extraction errors, edge cases, and patterns that require human expertise to resolve

• Work closely with engineers to improve model accuracy iteratively, communicating in the precise language of data structure and extraction logic

Partner with Product and Clients

• Work side-by-side with Product and Engineering — you bring the business logic, they bring the code, and together you raise the capability of the platform

• Participate in client-facing conversations as Canoe’s authority on asset data — you can explain the complexity and the solution in the same breath

• Translate field-level observations into product feedback that drives roadmap prioritization

What We’re Looking For:

• Deep, hands-on experience in private markets asset data — within an LP, fund of funds, family office, fund administrator, or data service provider with direct exposure to underlying asset-level reporting (we’re open on years; depth of exposure matters more than time in seat)

• Fluency across multiple asset classes: PE/VC, real assets, private credit, infrastructure — you understand how reporting conventions differ and why

• You have spent your career in this data — you know the “why” behind every valuation adjustment and the relationship between portfolio company financials and fund-level reporting

• Experience leading or mentoring a team — you know how to develop people and hold a high bar simultaneously

• A builder’s mindset: you are energized by taking a messy, manual industry problem and solving it with systemic logic

• Technical instincts without needing to write code — you understand data structures, can define strict extraction rules, and communicate precisely with engineers

• Uncompromising standards — you know what audit-ready looks like and you refuse to compromise on precision

• Startup energy — you are comfortable with ambiguity, motivated by ownership, and excited to define the function rather than inherit it