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

Governance & Partnership

• Partner with Cloud Engineering to design and drive public cloud spend management programs, including guardrails on provisioning and spend approval thresholds
• Participate in architecture and design reviews where infrastructure decisions carry meaningful cost implications by bringing cost tradeoffs into the conversation before decisions ship
• Identify and drive optimization opportunities (rightsizing, waste elimination, commitment planning) in partnership with engineering owners

Analysis & Ad Hoc Support

• Prepare ad hoc financial and cost analysis to support planning cycles, budget reviews, and executive asks
• Perform unit-economic analysis on new AI features to project cost-at-scale, enabling data-driven conversations with Product and Engineering teams regarding ROI and feature viability.

Requirements

Must-have

• 5-7+ years in FinOps, cloud cost management, or a closely related financial/technical analyst role
• Has built a cost attribution model from scratch not just maintained or updated one built by someone else
• Understanding of AI/LLM cost drivers: Deep familiarity with token-based pricing, model selection,, and the architectural levers that impact spend; ability to translate technical AI usage metrics into financial forecasts. Ability to
• AI-Enabled Automation: Demonstrated ability to use AI tools (e.g., custom GPTs, Claude, or agentic workflows) to automate repetitive data analysis, reporting tasks, or internal workflows
• Track record of influencing engineering/architecture decisions on cost grounds
• Hands-on experience with cloud cost tools (AWS Cost Explorer required; CUDOS dashboards, CloudHealth, Cloudability, Vantage, or similar)
• AWS Compute Optimizer: Knowledge of how to use this service to identify right-sizing opportunities for EC2, Lambda, and EBS based on utilization metrics.
• AWS Budgets & Cost Anomaly Detection: Experience in setting up automated alerts and responding to usage spikes before they hit the monthly bill.
• Working SQL proficiency able to independently query and reconcile cost data, not just consume pre-built dashboards
• Experience pulling data via APIs from cloud/cost platforms
• High proficiency in Excel/Google Sheets for financial modeling; comfort building executive-ready decks (PowerPoint/Slides)
• Demonstrated experience presenting cost/technical findings to non-technical executives or leadership, translating technical detail into business narrative
• Excellent organization, time management, and communication (written and verbal) skills

Strongly preferred

• Experience building financial models for variable-cost services (e.g., cloud API usage, token-based pricing) and translating technical usage patterns into business-level spend forecasts.
• Savings Plans and Reserved Instance cost attribution, so underlying resource usage changes are properly allocated to the teams that own the resources
• Basic infrastructure literacy and understanding what drives cost in compute, storage, networking, and containerized/K8s environments well enough to ask informed questions in architecture discussions
• Experience working in a multi-product or multi-business-unit environment where cost attribution across units was a core challenge
• Exposure to PE-backed or investor-scrutinized environments where financial defensibility of technical spend mattered
• Familiarity with AI/LLM API cost structures (token-based pricing, usage tiers) as this becomes a growing spend category