FinOps Lead

FinOps Weekly; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

FinOps Weekly in the United States is seeking a senior FinOps Lead to establish visibility, governance, and accountability for public cloud and AI spend across product lines. You will build cost attribution models, forecasts, and governance mechanisms linking costs to products, teams, and ROI.

Reporting to the Senior Director, Product Engineering Operations, you will partner with Cloud Engineering, Product, Finance, and engineering leadership to influence architecture and investment decisions,

Qualifications

• 5–7+ years in FinOps, cloud cost management, or a related financial/analytical role.
• Experience building a cost attribution model from the ground up.
• Deep understanding of AI/LLM cost drivers, including token pricing and model selection.

Responsibilities

• Build and improve cost attribution models for cloud and AI spend by product line, team, and environment.
• Establish tagging standards and close attribution gaps.
• Develop shared allocation models for Savings Plans and Reserved Instances.
• Forecast cloud and AI spend and monitor forecast drivers.
• Own monthly and quarterly cost reporting for leadership.
• Identify optimization opportunities—rightsizing, waste reduction, and cost governance.

Skills

FinOps

Cloud cost mgmt

AI cost drivers

SQL

Excel

Executive comms

Tools

AWS Cost Explorer

CUDOS dashboards

CloudHealth

Cloudability

Vantage

AWS Compute Optimizer

AWS Budgets

Cost Anomaly Detection

Job description

About the Role

This senior individual-contributor role will establish visibility, accountability, and governance for public cloud and AI spend across multiple product lines. The role will build cost attribution models, forecasts, and governance mechanisms that connect infrastructure and AI costs to products, teams, environments, and ROI. Reporting to the Senior Director, Product Engineering Operations, the FinOps Lead will partner with Cloud Engineering, the CPO’s organization, Product, Finance, and engineering leadership. The role will influence architecture and engineering decisions by bringing cost, efficiency, and financial trade-offs into planning and design discussions. The position focuses on building defensible cost models and optimization programs—not simply monitoring cloud bills. Its work will support executive reporting, budget planning, feature-level cost analysis, and more informed investment decisions.

Key Responsibilities

• Build and continuously improve cost attribution models for AWS and other public cloud spend by product line, team, and environment.
• Establish tagging standards, audit compliance, and close attribution gaps.
• Develop shared allocation models for Savings Plans, Reserved Instances, network charges, and team-level cost drivers.
• Advance feature-level cost attribution linking infrastructure spend to product ROI.
• Create optimization programs with measurable savings targets, including rightsizing, waste elimination, infrastructure modernization, and commitment planning.
• Forecast cloud and AI/LLM API spend against budget and monitor forecast drivers.
• Develop predictive cost models for AI/LLM services based on token usage, model selection, and variable consumption patterns.
• Own monthly and quarterly cost reporting for engineering leadership and executive audiences.
• Track discount utilization and identify Reserved Instance and Savings Plan roll-off risks.
• Partner with Cloud Engineering to establish provisioning guardrails and spend approval thresholds.
• Participate in architecture and design reviews where infrastructure choices have material cost implications.
• Prepare financial and cost analyses for planning cycles, budget reviews, and executive requests.
• Perform unit-economic analysis for new AI features to estimate cost at scale and support ROI and feature-viability decisions.
• Use AI tools and automated workflows to streamline data analysis, reporting, and internal processes.

Required Skills

• 5–7+ years of experience in FinOps, cloud cost management, or a closely related financial or technical analyst role.
• Demonstrated experience building a cost attribution model from the ground up.
• Deep understanding of AI/LLM cost drivers, including token-based pricing, model selection, and architectural factors affecting spend.
• Ability to translate technical AI usage metrics into financial forecasts.
• Demonstrated use of AI tools such as custom GPTs, Claude, or agentic workflows to automate analysis, reporting, or internal workflows.
• Experience influencing engineering or architecture decisions based on cost considerations.
• Hands-on experience with cloud cost tools, including AWS Cost Explorer; experience with CUDOS dashboards, CloudHealth, Cloudability, Vantage, or similar tools.
• Knowledge of AWS Compute Optimizer for identifying EC2, Lambda, and EBS rightsizing opportunities using utilization metrics.
• Experience configuring AWS Budgets and Cost Anomaly Detection alerts and responding to usage spikes.
• Working proficiency in SQL, with the ability to query and reconcile cost data independently.
• Experience retrieving data through APIs from cloud or cost-management platforms.
• Advanced proficiency in Excel or Google Sheets for financial modeling.
• Ability to create executive-ready presentations in PowerPoint or Slides.
• Experience presenting cost and technical findings to non-technical executives or leadership.
• Ability to translate technical details into a clear business narrative.
• Excellent organizational, time-management, written, and verbal communication skills.

Preferred Skills

• Experience building financial models for variable-cost services such as cloud APIs and token-based pricing.
• Experience translating technical usage patterns into business-level spend forecasts.
• Experience attributing Savings Plan and Reserved Instance costs to the teams responsible for the underlying resources.
• Basic infrastructure literacy across compute, storage, networking, and containerized or Kubernetes environments.
• Experience working in multi-product or multi-business-unit environments where cross-unit cost attribution was a core challenge.
• Experience in a private-equity-backed or investor-scrutinized environment where technical-spend defensibility was important.
• Familiarity with AI/LLM API cost structures, including token-based pricing and usage tiers.

Cloud Platforms & Technologies

Cloud Providers

• AWS
• Public cloud platforms

FinOps & Cloud Cost Management

• AWS Cost Explorer
• CUDOS dashboards
• CloudHealth
• Cloudability
• Vantage
• AWS Compute Optimizer
• AWS Budgets
• AWS Cost Anomaly Detection
• Savings Plans
• Reserved Instances

Compute & Infrastructure

• EC2
• Lambda
• EBS
• Compute
• Storage
• Networking

Containers

• Kubernetes
• Containerized environments

Data, Automation & Analysis

• SQL
• APIs
• Excel
• Google Sheets
• PowerPoint
• Slides
• Custom GPTs
• Claude
• Agentic workflows

FinOps Responsibilities

• Establish product-, team-, environment-, and feature-level cloud cost attribution.
• Govern tagging standards and improve cost allocation accuracy.
• Forecast and report AWS, public cloud, and AI/LLM API spending.
• Model variable AI costs based on token consumption, model selection, and usage patterns.
• Drive rightsizing, waste reduction, infrastructure modernization, and commitment optimization.
• Monitor Savings Plan and Reserved Instance utilization and roll-off exposure.
• Introduce provisioning guardrails and spend approval thresholds.
• Connect infrastructure and AI spend to product ROI and unit economics.
• Support architecture decisions, budget reviews, planning cycles, and executive reporting with defensible cost analysis.

Why You Might Be Interested

This role offers the opportunity to build FinOps and cost attribution capabilities from the ground up across cloud infrastructure and AI products. You will work directly with engineering, product, finance, and executive stakeholders while influencing architecture and investment decisions. The position combines financial modeling, technical analysis, automation, forecasting, and strategic cost governance.