Senior FinOps Engineer, Global Data & Analytics

ABOUT THE ROLE

AI is generating cost at a scale and complexity that traditional FinOps tooling was not built for — and this role exists to solve that problem. As FinOps Engineer, you will design and maintain the data pipelines that move metered AI usage from our internal AI Studio platform into clean, trusted, reporting-ready datasets. You will build and automate the dashboards that surface weekly exceptions and drive monthly billing accuracy, ensuring that chargeback across business units, platforms, applications, models, and environments is always defensible and timely. This is a hands-on engineering role with direct visibility into how the organization understands and governs its AI investment.

WHAT YOU'LL DO

Pipeline Engineering & Data Architecture

• Design, build, and maintain data pipelines that ingest metered usage data from AI Studio and API-sourced datasets, transforming raw consumption signals into normalized, reporting-ready structures

• Ensure spend is accurately attributed at every relevant dimension — business unit, platform, application, model, and environment — with consistent lineage and auditable methodology

• Integrate data from multiple sources (AI Studio, cloud billing APIs, Snowflake, Databricks) into a coherent, trusted cost dataset that Finance and Engineering teams can rely on for decisions

• Implement data quality controls, reconciliation checks, and anomaly detection logic to catch attribution errors, missing data, and billing discrepancies before they reach downstream consumers

• Maintain pipeline reliability and timeliness — cost data that arrives late or with gaps is not useful for governance; build with that constraint in mind

• Evolve the data architecture as new AI platforms, models, and usage patterns come in scope, keeping the system extensible without accumulating technical debt

Reporting & Dashboard Automation

• Build and automate Power BI dashboards that track weekly cost exceptions — surfacing anomalies, budget variances, and unallocated spend with enough context that owners can act without additional investigation

• Develop monthly billing metrics dashboards that drive chargeback processing, giving Finance clear visibility into actuals by business unit and cost category

• Automate recurring reporting workflows — month-end close support, variance summaries, trend analysis — reducing manual effort and eliminating recurring rebuild cycles

• Design reports that serve multiple audiences: technical detail for engineering owners, financial summaries for Finance partners, and executive-ready rollups for leadership

• Instrument unit economics views — cost per model, cost per application, cost per environment — that help teams understand the financial weight of their AI choices

Cost Attribution & Chargeback