Senior AI FinOps Data Analyst

Experience building automations, internal tools, or data pipelines — including use of AI-assisted development tools (e.g., Cursor, LLM APIs)

Familiarity with enterprise data platforms (Snowflake, Dataverse, or similar warehouse/lake environments)

Experience modeling OpEx and CapEx together for hybrid cloud or infrastructure planning

Exposure to AI/GPU workload cost forecasting or high-growth technology spend environments

Experience partnering with shared services, procurement, or finance teams on invoice validation, chargeback, or PO lifecycle management

Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience.

The Senior FinOps Data Analyst sits at the intersection of Cloud Infrastructure, Corporate Finance, and AI Engineering.

This is not an administrative or passive reporting role; it requires an AI thought-first mindset and a deep curiosity for building internal tools that eliminate manual friction.

Rather than relying on a single legacy platform, this analyst treats FinOps as a dynamic, multi-tool environment—leveraging raw operational datasets to power AI agents, predict cloud economics, and steer the enterprise's hybrid cloud strategy.

AI & GPU Infrastructure Modeling: Track, evaluate, and forecast cross-environment financial trends driven specifically by AI platform adoptions, machine learning workloads, and enterprise GPU utilization.

Dataverse Pipeline Integration: Act as the core architect for cloud and data center data collection, building reliable ingestion loops that funnel technical usage data directly into Dataverse.

Dynamic PO Lifecycle Management: Reform the purchase order framework from an annual forecast model to an active, twice-a-year allocation refresh cycle that keeps pace with AI investment spikes.

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Benefits

●    Comprehensive medical, dental, and vision coverage