FinOps

About the Role

Redwood is scaling public cloud infrastructure and AI features across multiple product lines, and we need a FinOps Lead to bring rigor, visibility, and accountability to that spend. This is a senior individual-contributor role with the authority to drive cross-functional cost governance directly. This role owns the translation of raw cloud cost data and AI spent into the models, forecasts, and governance mechanisms that let engineering, product, and executive leadership make informed decisions. This is not a bill-monitoring role. You will build the cost attribution infrastructure that ties cloud/AI spend to specific product lines and, ultimately, to ROI involving architecture and engineering teams to make the right trade offs and decisions in line with the strategic roadmap. 

The role will report to the Senior Director, Product Engineering Operations, and work closely with Cloud Engineering, the CPO's org, and engineering leadership to create cost visibility and defensibility informing leadership on efficiency strategies. Your ability to be an effective communicator, collaborative team player, and analytical thinker will be keys to success in this role.

Responsibilities

Cost Visibility, Attribution & Optimization

• Own and continuously improve cost models that attribute AWS (and other public cloud) spend by product line, team, and environment
• Drive tagging governance and hygiene, define standards, audit compliance, and close attribution gaps that prevent accurate cost-per-product reporting
• Build shared cost allocation models to drive transparency into per-team cost drivers where there are prevalent savings plans, reserved instances and network charges
• Build toward feature-level cost attribution that connects infrastructure spend to product ROI, not just aggregate bill totals
• Create and manage optimization programs with achievable savings targets, including working across engineering and finance to rightsize, clean up and modernize infrastructure

Forecasting & Reporting

• Forecast cloud and AI/LLM API spend against budget; monitor forecast drivers and maintain accuracy over time
• Develop and maintain predictive cost models for AI/LLM services, factoring in variable consumption patterns (token usage, model selection) to improve long-term forecast accuracy
• Own recurring cost reporting cadences (monthly/quarterly) for engineering leadership and executive materials
• Track and report on discount utilization (Reserved Instances, Savings Plans) and flag roll-off risk before it hits the P&L