Program Manager, Applied Technology Services

Requisition ID # 174739 

Job Category: Project / Program Management 

Job Level: Individual Contributor

Business Unit: Energy Delivery

Work Type: Hybrid

Job Location: Danville; San Ramon

Department Overview

Applied Technology Services (ATS) has been providing technology-based, innovative, high-value services to the company for over 50 years. ATS is a multidisciplinary team of over 130 engineers, scientists, and technicians. The ATS vision is to be a forward-thinking, technological leader providing high-value solutions and services needed across the Company. ATS high value services also help proactively avoid future problems by specifying equipment, materials and methods that are best practices in the industry.

Position Summary

Leads the development, implementation, and continuous improvement of cross-functional programs for technology evaluation including laboratory and field testing and analysis services.  The successful candidate will ensure effective program execution through strong governance, data visibility, performance monitoring, and stakeholder alignment. Drives standardization of processes, establishes clear roles and accountability, and integrates workflows with enterprise data systems to enable consistent, scalable, and data-driven decision-making.

The selected candidate must live within PG&E’s service territory.

PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job.  The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, and internal equity.  Although we estimate the successful candidate hired into this role will be placed towards the middle or entry point of the range, the decision will be made on a case-by-case basis related to these factors.​

A reasonable salary range is:

Minimum Base Salary (Bay Area)            $85,000.00

Mid Base Salary (Bay Area)        $106,000.00

Maximum Base Salary (Bay Area)           $127,000.00

Job Responsibilities

• Maintains visibility to key program data, metrics, and performance trends; analyzes data to develop insights and recommendations for management
• Defines and tracks KPIs and performance metrics, supporting operating reviews and program governance to monitor risks and overall program health
• Develops and manages budgets, monitors performance against forecasts, and explains variances
• Leads cross-functional projects and initiatives, prioritizing efforts in resource-constrained environments
• Builds stakeholder alignment and accountability, collaborating across teams to resolve issues and drive program outcomes
• Leads development, standardization, and governance of programs and processes, including workflows, roles, and continuous improvement initiatives
• Establishes and maintains process documentation, tools, and standards to ensure consistency, quality, and scalability
• Drives integration of processes with enterprise systems and data sources

Qualifications

Minimum:

• Bachelor’s Degree or equivalent experience
• 3 years of job-related experience

Desired:

• Master’s degree
• Experience defining and tracking KPIs, metrics, and performance dashboards
• PMI-Project Management Institute or PMP-Project Management Professional certification
• Knowledge of current utility and energy industry issues, industry trends and political, economic and social issues affecting the market.
• Knowledge of energy industry applications to local level customers, including energy issues, customer energy efficiency applications, and general customer information.
• Proficient with Microsoft Office products including Word, Excel, Access, Visio, Project, PowerPoint, PowerBI
• Proficient with the use of Artificial Intelligence including writing structured prompts, workflow integration, assisted research, and application of machine learning on large datasets