We are looking for a Finance Manager to join the Infrastructure BizOps team. This role owns the planning backbone that turns product group (PG) capacity demand into a funded, paced, and budgeted plan of record: the annual Infra Long Range Plan (LRP) allocation cycle, the escalations and funding decisions that come out of it, and the quarterly pacing and order collection that keep PG spend aligned to plan. It is the neutral broker between product groups, Finance, Data Science, and Infrastructure capacity planning, translating between business goals, capacity units, and dollars. This is a high-visibility role, with outputs that inform leadership funding decisions, internal capacity allocations, and executive reporting.

Responsibilities

Run the annual capacity allocation cycle: collect product group demand, align it with infra supply, and establish the plan of record across the baseline, escalations, and efficiency
• Manage the capacity escalations process end to end: drive communications across all product groups and their finance partners, partner with the Infra capacity team to stage demand, and collaborate with Data Science on ROI evaluation for leadership funding decisions
• Partner with Infra Supply and Capacity Planning on quarterly pacing, feasibility modeling, and supply-chain constraint navigation with a focus on the impact to internal product groups
• Own the methodology that converts capacity demand into dollars, including the internal price list used to charge product groups for the infrastructure they consume. Maintain the cost allocations and the policies governing it for both owned data centers and public cloud
• Guide product groups through the quarterly capacity ordering cycle, reconcile their spend against approved budgets, and maintain the pricing and budget inputs the capacity planning system depends on
• Own the recurring reporting on demand, escalations, and what the supply plan can actually support, including the monthly bridge between the fixed planning price and actual unit costs
• Maintain the documentation, queries, and version control behind the plan data, so the numbers and the methodology behind them stay traceable and reviewable cycle over cycle

Minimum Qualifications
• 12+ years of experience in finance, business operations, capacity or supply planning, or other analytical fields with a financial focus
• Experience owning a recurring, deadline-bound planning cycle end to end, from input collection through a leadership-facing plan of record
• Experience translating between operational units and dollars, and defending that methodology to both finance and technical audiences
• Strong analytical judgment, able to separate the drivers that matter from noise and defend a number under senior scrutiny
• Advanced modeling skills, including building and maintaining models that other teams depend on
• Ability to drive alignment between cross-functional teams with competing priorities, without positional authority
• Verbal and written communication skills, with demonstrated ability to distill complex allocation logic into a page or less for an executive audience

Preferred Qualifications
• Experience supporting an earnings or board-level reporting cycle
• Familiarity with rate card or transfer pricing mechanics, including the difference between a locked planning price and a continuously refreshed operational price
• Ownership of an ROI or prioritization framework used to gate funding requests
• Experience in infrastructure finance, capacity planning, or in capital-intensive industries
• Experience with allocation methodology for shared or unattributed cost pools
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Expertise in SQL
• Background working alongside supply chain constraints such as component lead times and silicon timing
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ability to model infrastructure unit economics, total cost of ownership, and capacity-driven financial scenarios at scale