Technical Program Manager, Energy Risk
Google · San Francisco, CA, USA, New York, NY, USA · United States
- Employer
- Requisition id
- 98003712181445318
- First posted (employer ATS)
- (7d ago)
- First seen by this site
- 2026-09-28T14:48:56Z
- Last verified live
- 2026-10-06T01:18:15Z
- Source
- Employer career portal (google)
Job description
A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits
Learn more about
benefits at Google
.
Establish, manage, and refine frameworks to measure and mitigate complex risk exposures (e.g., price, shape and counterparty risks) across Google’s global, multi-gigawatt energy portfolio.
Translate risk analytics, quantitative metrics, and wholesale power market dynamics into actionable insights that shape Google's long-term energy strategy and infrastructure footprint.
Lead high-impact technical programs bridging Energy Origination, Procurement, Operations, Finance, and Data Science to embed robust risk frameworks into portfolio decision-making.
Provide strategic guidance to commercial transaction teams on optimal risk allocation, hedging, and mitigation strategies to ensure complex agreements fall within acceptable risk limits.
Collaborate with software and data engineers to build and scale automated, programmatic technical tools and analytics dashboards for portfolio and agreement evaluation.
Minimum qualifications:
Bachelor's degree in a technical or quantitative field (e.g., Engineering, Mathematics, Computer Science), or equivalent practical experience.
8 years of experience in program management.
Experience in technical program management, bridging the gap between quantitative risk modeling, financial analysis, and software/data engineering.
Experience in the energy sector, with wholesale power markets, renewable energy transactions, or utility-scale portfolio management.
Experience of energy agreement structuring, energy risk management principles, and quantitative analysis.
Preferred qualifications:
Master's degree in a quantitative field such as Mathematics, Statistics, Engineering, or a related discipline.
8 years of experience managing cross-functional or cross-team projects.
Expertise in major wholesale electricity markets (e.g., PJM, ERCOT, CAISO) and experience structuring complex transactions in multiple regions globally.
Proficiency in programming or query languages (e.g., Python, SQL, C++) to build, review, or scale advanced financial mathematics models, including option pricing, stochastic modeling, and Monte Carlo simulations.
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