Job description

Requisition ID # 175159 Job Category: Information Technology Job Level: Manager/Principal Business Unit: Technology & Security Work Type: Hybrid Job Location: Oakland Department Summary 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. Position Summary Within the Technology & Security Organization, the FinOps AI Data Principal will design, build, and scale the core FinOps 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 FinOps data products. You will enables AI showback/chargeback readiness, forecasting, anomaly detection, unit economics, and optimization insights through standardized, governed FinOps 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. Job Responsibilities • Build and enhance the FinOps 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, FinOps Data Lake integration, FOCUS-aligned AI data modeling, full-cost showback dependencies, and AI chargeback reporting foundations. • Standardize, simplify, and automate FinOps 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 SageMaker use cases. Qualifications 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 Desired Knowledge, Skills, and Abilities: • Data Engineering Expertise: 5+ years of experience building production-grade, distributed data pipelines • Cloud & FinOps Proficiency: Deep knowledge of multi-cloud billing mechanics (AWS CUR, Azure FOCUS datasets) and FinOps Framework principles. • AI Telemetry Experience: Proven experience tracking and modeling AI infrastructure costs, including LLM API usage, GPU clusters (e.g., NVIDIA Triton, Kubernetes), and vector database spend. • Modern Data Stack Mastery: Hands-on experience with cloud data warehouses and lakehouses such as Snowflake, Databricks, BigQuery, or Amazon Redshift. • Financial Data Modeling: Strong background in designing schemas for complex financial allocation, amortization, shared-cost showback, and forecasting. • Education & Certification: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field; FinOps Foundation Certified (FOCUS) and Professional designation desired. Compensation PG&E is providing the full salary/pay range for this position. The actual amount paid to an individual will be based on multiple factors, including, but not limited to, internal equity, specific skills, education, licenses or certifications, experience, market value, and geographic location. The range to reasonably expect will be around the minimum and the midpoint of the range. The final decision will be made on a case-by-case basis related to the factors above. This job is also eligible to participate in PG&E’s discretionary incentive compensation programs. • Bay Area Min $140,000 • Bay Area Mid: $189,000 • Bay Area Max: $238,000

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Requisition ID # 175159
Job Category: Information Technology
Job Level: Manager/Principal
Business Unit: Technology & Security
Work Type: Hybrid
Job Location: Oakland
Department Summary
Build and enhance the FinOps data set, data model, reporting pipeline, and AI usage/cost telemetry integrations.
Enable mandatory metadata ingestion, FinOps Data Lake integration, FOCUS-aligned AI data modeling, full-cost showback dependencies, and AI chargeback reporting foundations.
Standardize, simplify, and automate FinOps 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 SageMaker use cases.
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 & FinOps Proficiency: Deep knowledge of multi-cloud billing mechanics (AWS CUR, Azure FOCUS datasets) and FinOps Framework principles.
AI Telemetry Experience: Proven experience tracking and modeling AI infrastructure costs, including LLM API usage, GPU clusters (e.g., NVIDIA Triton, Kubernetes), and vector database spend.
Modern Data Stack Mastery: Hands-on experience with cloud data warehouses and lakehouses such as Snowflake, Databricks, BigQuery, or Amazon Redshift.
Financial Data Modeling: Strong background in designing schemas for complex financial allocation, amortization, shared-cost showback, and forecasting.
Education & Certification: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field; FinOps Foundation Certified (FOCUS) and Professional designation desired.

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aillmmachine learningawsazureapikubernetes

Platform: successfactors
Effort: medium