Strategic Finance Operator, Data

About the role

The "Strategic Finance Operator, Data" builds the financial data infrastructure that all of our companies depend on. This role requires a blend of finance and technology skills, with a focus on automating financial processes and communicating insights effectively.

Responsibilities

• Build the data infrastructure all companies depend on
• Create dashboards, models, automated reports, and data checks that are replicated across every company we launch
• Combine data expertise with financial and accounting acumen to automate workflows such as revenue recognition, cohort analysis, and reconciliations
• Develop automated financial alerts to catch problems early and surface them automatically
• Grow into communicating what you build as the foundation matures
• Handle the full financial surface area for any given company, including valuations, tax considerations, SPV distributions, inter-company billing, and funding proposals
• Be a finance person first, able to read a P&L, understand unit economics, and know when a number is wrong
• Operate in a multi-company environment, finding differences in financial contexts across multiple businesses
• Build systems rather than just doing the work, leaving functions better than when you found them

Requirements

• 3-6 years of FP&A, financial analysis, or a finance-adjacent analytical role
• Experience reading a P&L, understanding unit economics, and explaining what a number means in business terms
• SQL skills to answer financial questions, not just pull data
• Experience building cohort models from raw data
• Experience working in a multi-entity environment
• Experience with Aleph, Looker, Metabase, or a comparable FP&A or BI platform at the build level
• Experience automating reporting processes that used to require manual work
• Enough accounting literacy to spot and correct miscoded transactions

Qualifications

• Understand revenue recognition across at least one business model well enough to defend it to an auditor
• Know the difference between technically correct and financially correct outputs
• Built something from messy, real-world financial data, not a clean dataset
• Willingness to go above and beyond to help achieve ambitious goals