Commercial Data Lead - Revenue and ARR Analytics

About the role

Our client is private equity investor backing growth-stage software companies. Their portfolio companies have strong product-market fit but, being young, lack consistent commercial KPI definitions — so there is no clear, comparable view of performance. This hands-on interim role brings standardized, board-grade commercial reporting into these companies one at a time, starting with ARR and "snowball" analytics, until the reporting is built, trusted, and handed over.

What you'll do

• One company at a time. Embed with a portfolio company and take it through the data maturity curve; each company is a discrete project, worked back-to-back with the next.
• Build the numbers. Create unified commercial reporting from the ground up, starting with ARR and snowball analysis (New, Upsell, Downsell, Churn) to give leadership an accurate view of performance.
• Find the truth. Work out who to ask and where the data lives; partner directly with the CFO or C-1 level and the company's data team where one exists.
• Get it right. Pull together, clean and reconcile data from disparate systems (billing, general ledger, contracts, CRM) into a single source of truth reconciled to the finance ledger — accuracy is the most important outcome.
• Hand over. Once built and validated, transfer ownership to the portfolio company and move to the next project.

What we expect

• Finance & data expertise. Deep expertise in finance and in data and its analysis — able to stand behind your numbers and push back with C-level stakeholders on the strength of that expertise.
• SaaS metrics fluency. Confident with ARR, NRR, churn, snowballs and billings-to-revenue reconciliation.
• Business x technical. Genuinely comfortable at the intersection of business and technical — equally at ease with a commercial conversation and a hands-on build. This matters most; it is not a pure data-engineering role.
• Hands-on delivery. Able to build analyses end-to-end on a modern data stack — Python and SQL, in environments such as Databricks, Azure or GCP — working independently across companies with or without their own data teams. Tooling around the core can be handled pragmatically, including with AI.

Engagement details

• Format. Interim contract, full-time capacity; back-to-back projects across the portfolio. Individual projects run from ~3 months to over a year depending on size and complexity.
• Location. Fully remote. Occasional travel (a few days per month) is welcome but not a hard requirement. Some companies may provide their own device.
• Team & growth. First of several sequential hires as the portfolio scales. Start ASAP; a ramp-up curve is expected.