HOME Cloud & Infrastructure Engineering Data Center Central Planning Lead
  • Meta
  • Menlo Park, CA
  • Full-Time
  • 33 days ago
Meta VERIFIED EMPLOYER

Data Center Central Planning Lead.

Cloud & Infrastructure Engineering Full-Time

Data Center Central Planning Lead: our view in 3 lines...

  • The Role:This role leads long-range data center capacity planning for Meta’s Strategic Planning organization.
  • The Person:The person will build capacity plans and quantitative models, translate workload growth into facility and electrical requirements, assess system constraints, and prepare executive decision packages for senior leaders.
  • Requirements:The ideal candidate has a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience, plus experience integrating AI tools, responsible and ethical AI practices, and ongoing AI skill development.

About the role

We are looking for a Data Center Central Planning Lead to join Meta's Data Center Strategic Planning organization. In this role, you will own and lead the development of end-to-end, constraint-aware data center capacity plans that translate product growth, AI workload evolution, and fleet architecture into executable roadmaps across power (MW), cooling, space, and network.

You will operate at the intersection of capacity engineering, portfolio planning, and investment decisioning, building quantitative models and scenario plans that inform multi‑billion‑dollar infrastructure choices. You will be expected to go deep in the data and engineering constraints while also serving as a trusted advisor to senior leaders, shaping direction through clear recommendations, executive-ready narratives, and cross‑org alignment.

This is an individual contributor leadership role: success is defined by technical rigor, durable planning mechanisms, and sustained influence across organizations—including the ability to advise, challenge, and align senior stakeholders.

Responsibilities

  • Own long-range capacity planning and strategy (LRP) to connect demand signals (product + AI) to data center supply plans and execution paths across MW delivery, space readiness, and network capacity—including multi‑year roadmaps, supply and demand matching, and key decision points
  • Build and maintain quantitative planning models (supply/demand, portfolio allocation, sensitivity analysis, bottleneck identification, schedule-risk and uncertainty modeling), and use them to drive investment prioritization and sequencing
  • Translate workload characteristics (GPU/AI clusters, rack power density, resiliency tiers, topology/interconnect needs, placement constraints) into facility and electrical requirements including MW ramp profiles, readiness gates, and deployment constraints
  • Lead assessment of system-level constraints across fleet and pipeline (utility delivery timelines, substation/transformer capacity, cooling technology limits, heat rejection, building envelopes, network ingress/egress, construction sequencing) and drive mitigation strategies with clear owners, timelines, and success criteria
  • Develop executive decision packages: synthesize complex analyses into crisp trade-offs and recommendations (cost, schedule, reliability, operability, risk), explicitly calling out assumptions, sensitivities, and "what changes the answer."
  • Advise and influence senior Meta leadership by framing options, aligning stakeholders pre‑readout, and driving decisions in forums with competing priorities—while maintaining technical credibility with engineering partners
  • Drive cross-functional execution and accountability: align roadmaps, surface gaps early, manage escalations, and ensure closure through programmatic follow-through and planning governance
  • Establish and continuously raise the planning "bar": define assumption governance, model review cadence, metric definitions, documentation standards, and reproducibility so plans are auditable, repeatable, and resilient to ambiguity
Minimum Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Preferred Qualifications

  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

$219,000/year to $301,000/year + bonus + equity + benefits

Published October 2, 2026
Location Menlo Park, CA
Job Type Full-Time