Finance Manager, DaaS

About this opportunity

About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About the role Reporting to the Senior Director of Finance, the DaaS Finance Manager is a critical member of the Finance org and a dedicated finance partner to the DaaS business as we scale. You will drive the financial insights that lead to better decisions: what we charge, what it costs to deliver, how much we need to deliver, and how it all rolls into revenue and ARR. The job is not just reporting numbers, but telling leadership what they mean and what to do about them. You will partner closely with the operating teams (Sales, Delivery Ops and RevOps) and bring finance rigor to how we work: clear assumptions, consistent definitions, and models people trust. Success is measured by outcomes, not outputs: deals priced to target margin, staffing plans that match demand, and a forecast leadership has confidence in. Key responsibilities The work falls into three pillars, with the mix shifting through the quarter. 1. Revenue model and forecast (with RevOps) Maintain the DaaS revenue model: bookings, acceptance-based revenue and ARR by deal, with scenario cases for planning. Partner with RevOps on the weekly revenue and ARR forecast, and run the weekly forecast deck and summary for leadership. Reconcile the field's call (commit, forecast, best case) with a deal-by-deal finance view; quantify coverage, swing deals and risks. Track delivery pacing and acceptance against plan and identify execution risks. Build bridges and variance analysis (forecast vs actual, bookings to revenue) for quarterly and annual planning. Build the DaaS inputs to the annual operating plan and long-range plan: revenue, cost of delivery, margin and delivery headcount, with monthly actuals-vs-plan variance analysis. Partner with Accounting on revenue recognition inputs, COGS and margin actuals. 2. Delivery capacity model Run and maintain the delivery capacity model in partnership with Delivery Ops, covering utilization, project mix, headcount per project and cost to deliver. Reconcile the capacity plan with actual staffing data and explain variances in mix, productivity and headcount. Translate the bookings pipeline into staffing demand and flag hiring or Expert Contributor needs ahead of the quarter. Lead a regular capacity planning review with Delivery leadership. 3. Business insights, pricing and strategic projects Turn the data into decisions: surface trends in pricing, mix, margin and pacing that leadership has not asked about yet, with a clear recommendation. Deal economics: partner with Deal Desk, which owns pricing, as deals need finance input; review deal models and pricing templates for accuracy and margin, and recommend adjustments. Customer and product profitability: cost to deliver by customer, project and product, tied to revenue. Product economics: what it takes for each product to hit margin targets, and where to invest next. Find broken or slow processes and recommend and drive fixes that shorten cycle time and improve data quality. Automate what repeats: move recurring models from manual spreadsheets toward scalable, auditable tools. Support board and investor content for the DaaS business. What success looks like By month six this person runs the weekly forecast and capacity model with the senior leader reviewing rather than building, and Deal Desk and RevOps rely on them to check deal economics. First 30 days: Meets the team and learns the DaaS model end to end: pricing, capacity, revenue and ARR. Shadows Deal Desk reviews and the forecast call. Co-produces one weekly forecast deck. First 60 days: Runs the weekly forecast deck and summary. Reviews Deal Desk models and templates independently. Leads one monthly capacity review with Delivery Ops. First 90 days: Recommends at least one fix to a Deal Desk or RevOps pricing template. Reconciles the capacity model to weekly staffing data with a documented variance bridge. Delivers a first customer or product profitability view. First year: Forecast accuracy and pacing calls trusted by the exec team. Margin and capacity assumptions refreshed every quarter without fire drills. Recurring models automated. Qualifications We want a builder who is equally comfortable in a deal negotiation thread and a 30-tab model, ideally with experience in a services-heavy, usage-based or manufacturing business. Must-haves 5–8 years in FP&A, product or business finance, deal desk, or strategic finance; at least 2 at a high-growth tech company. Hands-on ownership of a revenue forecast and a unit-economics or pricing model, not just review of one. Fluency in gross margin vs contribution margin, cost-to-serve, and how pricing, discounting and incentives flow into both. Experience with services or labor-based delivery economics: utilization, headcount per project, contractor mix. Expert spreadsheet modeling (Google Sheets / Excel) and working SQL; comfortable pulling from a CRM and a data warehouse. Clear, concise executive communication: can write a five-line summary leadership can act on. Comfort with ambiguity: makes a reasoned call with incomplete data, states the assumptions, and refines as facts arrive. Process mindset: spots friction, proposes a simpler way, and drives it to adoption rather than working around it. Bachelor's degree in finance, economics, engineering or a related field. Nice-to-haves Experience in AI/ML, data services, a marketplace with reusable inventory (resale economics), services or manufacturing. Familiarity with ARR-style metrics and acceptance-based revenue recognition. Board or investor materials experience, including fundraise data rooms. Uses AI tools and light scripting (Python) to automate recurring analysis. Experience with BI tools (e.g., Looker, Tableau) or planning tools (e.g., Adaptive, Pigment). Background at a top consulting, banking or finance-rotation program. Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location. Salary range(s) for this role $130,000 — $180,000 USD Be Your Best at Snorkel Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success. Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

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