Drimva

Meta

Program Development Manager, Capacity Planning

$170K–$238K

Menlo Park, CA · Manager · Full-time · Posted Sep 23, 2026

Experience
8+ years; the posting allows capacity planning, infrastructure planning, supply chain planning, or a closely related program management discipline.
Education
Bachelor's degree in a directly related field, or equivalent practical experience.

About the job

Owns the frameworks, processes, and tools Meta uses to forecast, allocate, and deliver global compute, network, and storage capacity, while aligning infrastructure plans with demand and investment decisions.

What you'll do

  • Lead capacity-planning frameworks, methodologies, governance, and tools across Meta’s global data center fleet.
  • Coordinate cross-functional capacity plans and translate them into capital expenditure forecasts and executive recommendations.
  • Improve planning visibility, forecast accuracy, delivery predictability, and cycle time; mentor planning professionals.

What they ask for

  • Bachelor's degree in a directly related field, or equivalent practical experience.
  • 8+ years of experience in capacity planning, infrastructure planning, supply chain planning, or a closely related program management discipline within a large-scale technology or data center environment.
  • Experience leading cross-functional programs and developing global or multi-region planning frameworks, forecasting, or supply-demand balancing processes.
  • Experience translating infrastructure plans into capital expenditure forecasts and communicating investment trade-offs to executive audiences.

Nice to have

  • Experience in Design and Construction of Data Centers.
  • Experience with compute, network, or storage capacity planning in a hyperscale cloud or data center environment, including hardware procurement lead times and supply chain constraints.

Pay

$170,000/year to $238,000/year (base salary)

AI focus

Integrate AI tools to optimize or redesign workflows and improve efficiency or quality; develop AI skills such as prompt/context engineering and agent orchestration; apply responsible AI practices including risk assessment, bias mitigation, and quality and accuracy reviews.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)