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Temporal Technologies is seeking a Finance Director to own revenue forecasting and financial modeling for a consumption-based, high-growth developer tools business. This is a hands-on, execution-oriented role based in San Francisco that sits at the intersection of finance, data science, and go-to-market strategy.
You will own end-to-end revenue forecasting for consumption-based products, building and maintaining bottoms-up models that incorporate usage drivers, customer cohorts, ramp curves, contract structures, and expansion behavior. You'll partner closely with a dedicated Data Scientist to combine financial judgment and GTM context with statistical modeling, usage data, and predictive analytics. The role demands expert-level proficiency in Excel and Google Sheets, with daily iteration as new data, usage patterns, and GTM motions evolve.
Key responsibilities include: developing detailed financial models and scenario analysis; translating data science outputs into clear financial assumptions and executive-ready forecasts; working directly with Sales, RevOps, and GTM leadership to connect pipeline, bookings, usage, and revenue outcomes; analyzing ARR vs. consumption dynamics, net dollar retention, and expansion behavior by segment; supporting forecasting for new GTM motions, pricing changes, and product launches; and leading headcount and tooling investment planning.
You'll operate in a hypergrowth environment where historical data is imperfect and judgment matters. You'll help design forecasting processes that combine data-driven rigor with financial intuition, prepare materials for QBRs and board discussions, and partner with Accounting on revenue recognition and variance analysis tied to usage-based billing.
The ideal candidate has early career training in investment banking, private equity, venture capital, or revenue-focused FP&A, followed by experience supporting a technology startup during hypergrowth, ideally with a consumption or usage-based revenue model. You must be deeply analytical, execution-oriented, and comfortable with ambiguity and rapid iteration.
