Quick overview
Job description
ABOUT THE ROLE
AI is generating cost at a scale and complexity that traditional FinOps tooling was not built for β 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 β business unit, platform, application, model, and environment β 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 β 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 β 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 β month-end close support, variance summaries, trend analysis β 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 β cost per model, cost per application, cost per environment β that help teams understand the financial weight of their AI choices
Cost Attribution & Chargeback
Own the technical implementation of the chargeback model: ensuring spend is tagged, allocated, and attributed consistently across all dimensions required for accurate billing
Partner with the FinOps Team Lead and Finance to translate business rules for cost allocation into pipeline logic and reporting outputs
Identify and remediate attribution gaps β untagged resources, missing ownership metadata, inconsistent naming β through automation rather than manual correction
Build alerting and threshold logic that flags exceptions in near-real-time, enabling the operating team to investigate and resolve before monthly close
Track and report on chargeback accuracy over time, building confidence in the numbers with Finance stakeholders who rely on them for business unit P&L
Practice Development
Contribute to the evolution of the FinOps data model as AI consumption patterns grow in complexity β new models, new providers, new usage types require a pipeline that keeps pace
Evaluate and adopt relevant standards (e.g., FOCUS β FinOps Open Cost and Usage Specification) to ensure the organization's cost data aligns with emerging industry norms
Document pipeline design, data definitions, transformation logic, and dashboard specifications in a way that makes the system maintainable and understandable across the team
Identify opportunities to automate manual processes upstream and downstream of the pipeline β reducing toil and improving the timeliness and reliability of cost data
Solid data engineering background: experience building and maintaining production data pipelines that ingest, normalize, and transform data from multiple sources at scale
Proficiency in Python, including data manipulation libraries (pandas, and comparable tools for working with structured and semi-structured datasets)
Strong SQL skills β able to write, optimize, and maintain queries against large datasets in modern data platforms
Experience with Snowflake and/or Databricks for data storage, transformation, and pipeline execution
Demonstrated ability to build dashboards and reports in Power BI that are trusted, used, and maintained β not just created and forgotten
Comfort working with API-sourced datasets: designing ingestion patterns, handling schema changes, and ensuring data freshness and completeness
Understanding of cloud billing data structures β how usage is metered, rated, and reported β sufficient to work effectively with raw cost and usage data
Preferred
Familiarity with Azure cost management data structures; experience with Azure Data Explorer (ADX) and Kusto Query Language (KQL) is a meaningful advantage in this environment
FinOps Certified Practitioner or FinOps Certified Engineer; working knowledge of the FinOps Foundation framework and cost management lifecycle
Experience with AI or LLM cost data β token consumption, model-level attribution, inference vs. training cost structures, API-based usage metering
Exposure to FinOps platforms (Apptio Cloudability, FinOut, or cloud-native cost tools) and how their data can be integrated with internal pipelines
Experience implementing tagging standards, cost allocation automation, or data quality enforcement in a cloud billing context
Background working in a chargeback or showback environment where accuracy and auditability of cost data are non-negotiable
KEY COMPETENCIES
Engineering discipline: Builds pipelines that are reliable, documented, and maintainable β not just pipelines that work once.
Data curiosity: Asks why the numbers look the way they do; does not surface data without understanding it.
Precision under ambiguity: Produces accurate, auditable outputs even when upstream data is messy, incomplete, or inconsistently structured.
Automation mindset: Defaults to building systems that run without manual intervention; treats recurring manual effort as a problem to solve.
Cross-functional communication: Translates technical data concepts into language that Finance and business stakeholders can act on.
Ownership: Treats the pipeline and dashboards as products β with reliability standards, change management, and accountability for quality.
The pay range for the role is $134,200 to $195,800. The specific offer will depend on an applicantβs 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.