Technical Program Manager III, Data Center Infrastructure, Cloud Supply Chain

Minimum qualifications:

• Bachelor's degree in a technical field, or equivalent practical experience.
  
• 5 years of experience in program management.
  
• Experience in data center infrastructure (i.e., power space, cooling, networking).
• Experience with AutoCAD and design documents.

Preferred qualifications:

• 5 years of experience managing cross-functional or cross-team projects.
• Experience with colocation capacity planning, including the use and automation of planning and optimization tools.
• Experience as an Electrical Engineer, Civil Engineer, Architectural, or Mechanical Engineering.
• Experience with data center, networking and machine deployments, including an understanding of deployment process.
• Familiarity with new product introduction (NPI) process and Product Life Cycle (PLC), particularly as related to hardware products for machine learning and high-performance computing.
• Ability to drive tools and automation for continuous process improvement.

About the job

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.

In this role, you will be focused on aligning new products and technical infrastructure interface roadmaps to enable fast, predictable, and optimized deployment of heterogeneous machines for all product areas. You will influence products to optimize the utilization of technical infrastructure, enhances tools and processes for predictability, and shape new colocation server floor designs, considering the unique constraints and opportunities of colocation environments. You will also focus on the deployment of Machine Learning (ML) infrastructure within colocation spaces.

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 $156,000-$229,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.

Responsibilities

• Support the delivery of large projects, managing data center constraints (e.g., power, space, cooling, operations and network) in colocation data center deployments.
• Create and manage physical layouts and standards, including the deployment impact and feasibility of New Product Introduction (NPIs). Define and manage tooling requirements for planning and delivery.
• Own and nurture relationships between cross-functional teams. Track deployment workflows with partner teams and raise issues proactively to ensure on-time follow-through.
• Influence the development of new products (NPI) and colocation server floor design decisions, focused particularly on upcoming fleetwide transitions in the Machine Learning (ML) and accelerators space. Build plans to help smoothly transition between successive product generations.
• Drive the development of new tools and decision-making frameworks for optimizing fleet life-cycle.