FinOps AI Data Engineer, Principal

Job Category:
Information Technology

Job Level: Manager/Principal

Business Unit:
Technology & Security

Work Type:
Hybrid

Job Location:

Oakland

Technology & Security designs, develops, operates, and maintains PG&E’s technology, telecommunications, and security capabilities that enable the company to meet its commitment to providing safe, reliable, and affordable service to customers.

Technology & Security partners with the business to strengthen capabilities through the development of new functionality, the implementation of modern and secure technologies, and the continuous improvement of systems and services. By reducing risk, increasing productivity, managing costs, and enabling organizational effectiveness, Technology & Security helps advance PG&E’s strategic priorities through trusted, innovative, and secure solutions.

Within the Technology & Security Organization, the Fin Ops AI Data Principal will design, build, and scale the core Fin Ops Data Platform. This platform powers AI telemetry, scalable reporting, cloud allocation, forecasting, optimization, and comprehensive AI cost visibility. You will enable enterprise-wide visibility into cloud and generative AI consumption by building trusted data pipelines, robust data models, and standardized, governed Fin Ops data products.

You will enables AI showback/chargeback readiness, forecasting, anomaly detection, unit economics, and optimization insights through standardized, governed Fin Ops data products.

The HQ location is flexible within the Bay Area Region and will be agreed upon by the hiring leader and successful candidate. The position is hybrid working from your remote office and in-person at the Oakland Corporate Office 1-2x per week or based on business needs or company requirements.

• Build and enhance the Fin Ops data set, data model, reporting pipeline, and AI usage/cost telemetry integrations.
• Translate billing, allocation, tokenomics, AI Gateway, AI DLC Tool, AI Agent Tool, LLM, workspace, user, AppID, use-case, order-number, model-provider, and model-name requirements into scalable data and reporting capabilities.
• Enable mandatory metadata ingestion, Fin Ops Data Lake integration, FOCUS-aligned AI data modeling, full-cost showback dependencies, and AI chargeback reporting foundations.
• Standardize, simplify, and automate Fin Ops reporting, AI showback/chargeback views, optimization insights, anomaly detection inputs, and process workflows.
• Expand data engineering capacity for Snowflake, PowerBI, Flexera, AI Gateway, external AI usage sources, and cloud provider cost data pipelines.
• Build and maintain AI usage telemetry, tokenomics data sets, allocation logic, unit economics inputs, and AI showback/chargeback reporting foundations.
• Automate data quality checks, reporting refreshes, anomaly detection inputs, optimization insight pipelines, and consumption trend data for cloud and AI spend.
• Support inventory and data capture for LLM usage outside the Enterprise AI Gateway, including known external GenAI and Sage Maker use cases.

Minimum:

• BA/BS in computer science, management information systems, or related field of study, or equivalent work experience
• 10 years of experience with multiple data engineering/ETL ecosystems
• 3 years of experience in machine learning algorithm deployment and machine learning operations
• Experience in leading development teams

• Data Engineering Expertise: 5+ years of experience building production-grade, distributed data pipelines
• Cloud & Fin Ops Proficiency:
  Deep knowledge of multi-cloud billing mechanics (AWS CUR, Azure FOCUS datasets) and Fin Ops Framework principles.
• AI Telemetry
  
  Experience:
  
  Proven experience…