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.
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
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
Cost Attribution & Chargeback
Document pipeline design, data definitions, transformation logic, and dashboard specifications in a way that makes the system maintainable and understandable across the team
Qualifications
Experience with AI or LLM cost data — token consumption, model-level attribution, inference vs. training cost structures, API-based usage metering
Precision under ambiguity: Produces accurate, auditable outputs even when upstream data is messy, incomplete, or inconsistently structured.
The pay range for the role is $134,200 to $195,800. The specific offer will depend on an applicant’s skills and other factors. This role may also be eligible to participate in a discretionary annual incentive program. Chubb offers a comprehensive benefits package, more details on which can be found on our careers website. The disclosed pay range estimate may be adjusted for the applicable geographic differential for the location in which the position is filled.
Chubb is a world leader in insurance. With operations in 54 countries, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance, and life insurance to a diverse group of clients. The company is distinguished by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise and local operations globally.
At Chubb, we are committed to equal employment opportunity and compliance with all laws and regulations pertaining to it. Our policy is to provide employment, training, compensation, promotion, and other conditions or opportunities of employment, without regard to race, color, religious creed, sex, gender, gender identity, gender expression, sexual orientation, marital status, national origin, ancestry, mental and physical disability, medical condition, genetic information, military and veteran status, age, and pregnancy or any other characteristic protected by law. Performance and qualifications are the only basis upon which we hire, assign, promote, compensate, develop and retain employees. Chubb prohibits all unlawful discrimination, harassment and retaliation against any individual who reports discrimination or harassment.
