Senior FinOps Engineer, Global Data & Analytics

Chubb•Philadelphia, PA

2d•$134,200 - $195,800

About The Position

The Senior FinOps Engineer designs and maintains data pipelines that move metered AI usage from Chubb’s internal AI Studio platform and API-sourced datasets into clean, trusted, reporting-ready datasets. The role supports defensible, timely chargeback across business units, platforms, applications, models, and environments, and builds automated reporting that helps the organization understand and govern its AI investment. The engineer integrates data from sources such as AI Studio, cloud billing APIs, Snowflake, and Databricks; implements quality controls, reconciliation checks, anomaly detection, and reliable data delivery; and evolves the architecture as new AI platforms, models, and usage patterns are added. The role also builds and automates Power BI dashboards for weekly cost exceptions, monthly billing and chargeback, recurring reporting, and unit economics, tailoring outputs to engineering, Finance, and leadership audiences. The position owns the technical implementation of the chargeback model, partners with the FinOps Team Lead and Finance on allocation rules, and automates remediation of attribution gaps. It contributes to the development of the FinOps data model, evaluates relevant standards such as FOCUS, documents pipelines and reporting specifications, and identifies automation opportunities that improve cost-data reliability and timeliness.

Requirements

Experience building and maintaining production data pipelines that ingest, normalize, and transform data from multiple sources at scale.
Proficiency in Python, including pandas or comparable tools for structured and semi-structured data.
Strong SQL skills, including writing, optimizing, and maintaining 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 and maintain trusted, used Power BI dashboards and reports.
Comfort working with API-sourced datasets, including ingestion design, schema changes, and data freshness and completeness.
Understanding of cloud billing data structures, including how usage is metered, rated, and reported.

Nice To Haves

Familiarity with Azure cost management data structures; experience with Azure Data Explorer (ADX) and Kusto Query Language (KQL).
FinOps Certified Practitioner or FinOps Certified Engineer certification, or working knowledge of the FinOps Foundation framework and cost management lifecycle.
Experience with AI or LLM cost data, including token consumption, model-level attribution, inference versus training costs, and API-based usage metering.
Exposure to FinOps platforms such as Apptio Cloudability, FinOut, or cloud-native cost tools, and integrating their data with internal pipelines.
Experience implementing tagging standards, cost allocation automation, or data quality enforcement in a cloud billing context.
Background in a chargeback or showback environment where cost-data accuracy and auditability are essential.

Responsibilities

Design, build, and maintain pipelines that ingest and transform metered AI usage and API-sourced data into normalized, reporting-ready datasets.
Attribute spend consistently across business units, platforms, applications, models, and environments, with clear lineage and auditable methodology.
Integrate data from AI Studio, cloud billing APIs, Snowflake, and Databricks into trusted cost datasets.
Implement data quality controls, reconciliation checks, and anomaly detection to identify attribution errors, missing data, and billing discrepancies.
Maintain pipeline reliability and timely delivery, and evolve the data architecture as platforms, models, and usage patterns change.
Build and automate Power BI dashboards for weekly cost exceptions and monthly billing metrics, including anomalies, budget variances, unallocated spend, actuals, and cost categories.
Automate recurring reporting workflows, including month-end close support, variance summaries, and trend analysis.
Design reporting for engineering owners, Finance partners, and leadership, and instrument unit economics views such as cost per model, application, and environment.
Own the technical implementation of the chargeback model and ensure spend is tagged, allocated, and attributed consistently.
Partner with the FinOps Team Lead and Finance to translate allocation business rules into pipeline logic and reporting.
Identify and remediate attribution gaps such as untagged resources, missing ownership metadata, and inconsistent naming through automation.
Build alerting and threshold logic to flag exceptions in near-real time and support investigation before monthly close.
Track and report chargeback accuracy over time for Finance stakeholders.
Contribute to the FinOps data model as AI consumption patterns grow and evaluate standards such as FOCUS.
Document pipeline design, data definitions, transformation logic, and dashboard specifications.
Identify opportunities to automate manual processes upstream and downstream of the pipeline.