Program Manager II, Infrastructure Capacity Management, Google Cloud

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 2 years of experience in program or project management.
  
• Experience in cloud capacity management.
• Experience with the data center and supply chain fields.

Preferred qualifications:

• MBA or technical degree.
• 5 years of experience in managing technically-testing projects.
• 2 years of experience managing cross-functional or cross-team projects.
  
• Ability to conceive of and accept responsibility for projects and see them through to completion.
• Excellent influencing and collaboration skills, with the ability to deliver solutions.

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 Program Manager at Google, you’ll lead complex, multi-disciplinary projects from start to finish — working with stakeholders to plan requirements, manage project schedules, identify risks, and communicate clearly with cross-functional partners across the company. Your projects will often span offices, time zones, and hemispheres. It's your job to coordinate the players and keep them up to date on progress and deadlines.

Our goal is to build a Google that looks like the world around us — and we want Googlers to stay and grow when they join us. As part of our efforts to build a Google for everyone, we build diversity, equity, and inclusion into our work and we aim to cultivate a sense of belonging throughout the company.

In this role, you will be responsible for coordinating across Google’s Engineering teams to solve issues in cloud computing. You will work with Google’s supply chain, data center infrastructure, networking, and software engineering teams to design and deliver the future state of computing resource management within Google. You will lead the automation of compute resource allocation for all software engineering initiatives at Google and balancing of resource distribution against supply chain, data center infrastructure, network, shared service, and budgetary constraints, and the implementation of tools and policies to optimize resource management across the engineering stack at Google.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $117,000-$172,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

• Support operations and analytic projects related to the allocation and budgeting of internal computing resources and drive efficiency programs.
• Support subsequent operations and analytic projects related to billing, service fulfillment, internal chargeback and price modeling, supply chain management, and capacity planning needs for compute and storage resources.
• Play a key role in the advancement of strategy, policy, and supporting tools for operations that provide scripting/tool development as well as requirements documentation.
• Work with partner teams to launch and deliver projects related to data architecture and process re-design around demand and supply-side compute resource management.