Senior Cloud & AI FinOps Analyst

Autodesk•Novi, MI

23h•$98,000 - $175,450

About The Position

Autodesk is seeking a Senior Cloud & AI FinOps Analyst to scale FinOps as a service for internal stakeholders. This role blends traditional multi-cloud FinOps execution with advanced analytics, Business Systems Analysis, and product ownership of our expanding AI asset portfolio. As generative AI tools become deeply embedded in our engineering and corporate workflows, you will strengthen our FinOps culture through shared accountability and transparency. You will build self-service capabilities and use automation to accelerate insight, recommendation, and optimization execution across major public cloud providers (AWS, Azure, GCP) as well as specialized SaaS AI platforms, developer tooling, and API foundation models (e.g., Claude, OpenAI ChatGPT, Cursor).

Requirements

Bachelor's degree in Finance, Accounting, Business Administration, Information Systems, Information Technology, or a related field, or equivalent practical experience
5+ years of experience as a Business Systems Analyst or FinOps Analyst, including requirements elicitation, process mapping, stakeholder facilitation, and shipping stakeholder-facing capabilities
Strong analytical problem-solving skills, including the ability to structure ambiguous problems, define metrics, validate data, and communicate defensible, data-driven conclusions
Hands-on experience building and maintaining dashboards in Apptio Cloudability, Apptio BI, Turbonomic, or closely comparable platform-native tooling
Working knowledge of cloud billing and pricing constructs across a multi-cloud environment (AWS, Azure, GCP), combined with an understanding of SaaS/AI licensing models (user seats, token-based usage, API pricing tiers)
Experience partnering with a technical community to drive infrastructure or SaaS lifecycle automation, including defining automation requirements, acceptance criteria, and measurable outcomes
Strong written and verbal communication skills, including technical writing for documentation and executive-ready storytelling
Demonstrated customer-focused service mindset, including setting clear expectations and continuously improving self-service visibility

Nice To Haves

FinOps Certified Practitioner (FCP) or higher
Cloud foundational certifications (AWS Cloud Practitioner, Microsoft Azure Fundamentals, or Google Cloud Digital Leader)
Experience establishing governance frameworks, provisioning workflows, or chargeback mechanisms for AI tools, LLMs, or developer productivity applications

Responsibilities

Serve as the FinOps Product Owner for stakeholder-facing capabilities, including multi-cloud and AI spend dashboards, data reporting, documentation, enablement, automation self-service, and intake workflows
Maintain a FinOps services portal as the primary self-service entry point, ensuring stakeholders can easily discover available cloud/AI services, understand optimization guidelines, and request automated license or budget help
Evolve the operating model for FinOps as a service, including roadmap development, backlog prioritization, and continuous improvement cycles for both cloud infrastructure and AI developer tools
Lead a FinOps Champion Network across engineering, finance, and product teams, establishing shared goals, playbooks, and office hours to improve stakeholder self-sufficiency
Drive cultural practices that improve governance and decision quality, including showback/chargeback adoption, KPI standardization, strict infrastructure tagging, and AI tool user-attribution expectations
Build, maintain, and iterate dashboards and reporting in Apptio Cloudability, Apptio BI, and Turbonomic, ensuring metric definitions, data quality checks, and stakeholder usability
Establish usage tracking, user seat auditing, and token consumption analytics for developer and corporate AI applications (e.g., Cursor, ChatGPT Enterprise, Claude) to identify underutilized licenses or inefficient prompt token usage
Develop repeatable analyses for spend anomalies, monthly variance, and key cost drivers (e.g., unexpected surges in third-party foundation model APIs or ETL tools). Translate these into actionable summaries for technical and non-technical leaders
Define and maintain reporting standards, allocation logic, and tagging/labeling requirements to ensure trust, consistency, and accurate cost attribution back to specific business units and cost centers
Identify cost optimization opportunities and productize them into repeatable, automated motions—including detection, recommendation generation, execution workflows (with approvals), and post-change verification
Support service owners in forecasting future cloud and AI tooling spend, tracking performance against budget, and producing variance narratives.
Manage and improve effective cloud rates and AI vendor agreements through commitments, negotiated pricing, and discount levers
Partner with cloud and automation engineering teams to implement guardrails that operationalize recommendations and prevent spend regressions