Director, Cloud GTM Finance

Responsibilities

• Own revenue forecasting across all Cloud and Managed AI products, bridging contracted commits and on-demand utilization
• Partner with RevOps and HR to design and operationalize scalable sales incentive plans and forecast compensation expenses
• Model economics for partnership, channel, and reseller arrangements, including revenue share and margin impact
• Own end-to-end financial reporting on Cloud GTM performance, defining frameworks for sales productivity and ROI
• Act as the financial voice for GTM performance to the executive team and Board of Directors
• Hire, coach, and develop a high-performing GTM Finance team and establish necessary systems and tooling
• Influence go-to-market strategy, segmentation, and territory design through analytical rigor

Requirements

• 10+ years of experience in Strategic Finance, FP&A, GTM/Sales Finance, Investment Banking, Private Equity, or Management Consulting
• 5+ years in GTM or Sales Finance at a high-growth technology company
• 3+ years of experience leading and developing teams
• Operating experience as an embedded finance partner to a senior GTM leader (CRO, CCO, or VP Sales)
• Deep fluency in committed-consumption revenue mechanics (committed vs. on-demand, bookings vs. revenue)
• Proven success in sales target setting, sales compensation design, and performance reporting
• Track record building pricing frameworks for complex enterprise contracts
• Expert-level financial modeling skills
• Bachelor's degree in Finance, Economics, Engineering, Mathematics, or a related analytical field

Preferred Qualifications

• Direct GTM or Sales Finance experience at a hyperscaler (AWS, Google Cloud, Azure) or cloud infrastructure provider
• Experience building a GTM Finance function from scratch
• Comfort with capex-intensive infrastructure businesses and capacity planning
• MBA or CFA

About the Company

Crusoe is a vertically integrated AI infrastructure company that owns and operates each layer of the stack to power the world's most ambitious AI workloads using an energy-first approach.