Data Center Central Planning Manager

We are looking for a Data Center Central Planning Manager () to join Meta’s Data Center Strategic Planning organization. In this role, you will drive the development of constraint-aware data center capacity plans that translate product growth, AI workload evolution, and fleet architecture into actionable roadmaps across power (MW), cooling, space, and network. You will work at the intersection of capacity engineering, portfolio planning, and investment decisioning, building quantitative analyses and scenarios that inform major infrastructure choices. You’ll be expected to go deep in the data and engineering constraints, partner effectively across Capacity Engineering, Facilities, Network, Construction, Site Selection, Finance, and Operations, and communicate clear recommendations that move decisions forward.

Responsibilities

• Drive portfolio capacity plans connecting demand signals (product + AI) to supply plans and execution paths across MW delivery, cooling envelope, space readiness, and network capacity, including near-to-mid term roadmaps and readiness milestones
• Build and maintain quantitative planning models (supply/demand, scenario analysis, sensitivities, bottleneck identification, schedule-risk and uncertainty modeling) to support prioritization and sequencing decisions
• Translate workload characteristics (GPU/AI clusters, rack power density, resiliency tiers, topology/interconnect needs, placement constraints) into facility and electrical requirements, including megawatt ramp profiles, readiness gates, and deployment constraints
• Assess system-level constraints across fleet and pipeline (utility delivery, substation/transformer capacity, cooling limits, heat rejection, building envelopes, network ingress/egress, construction sequencing) and drive mitigation plans in partnership with directly responsible teams
• Develop decision packages and narratives: synthesize analyses into clear trade-offs and recommendations (cost, schedule, reliability, operability, risk), explicitly calling out assumptions and sensitivities
• Partner with senior stakeholders by aligning pre-reads, surfacing key risks early, and supporting decision-making in planning forums—maintaining strong technical credibility with engineering partners
• Drive cross-functional execution: align dependencies, track closure of gaps, manage escalations as needed, and support planning governance (cadence, documentation, model hygiene)

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Bachelor’s degree in Engineering, Computer Science, Applied Math, Physics, Operations Research, or equivalent practical experience
• 8+ years of experience in data center / infrastructure / cloud / hardware environments, with significant work in capacity planning, infrastructure strategy, systems engineering, or large-scale technical planning
• Demonstrated ability to lead ambiguous, cross-functional problems to decision and execution; ability to create structure and drive progress through partners
• Strong quantitative background: experience building models, performing scenario and sensitivity analysis, and making recommendations under uncertainty
• Technical fluency across data center domains is sufficient to drive trade-offs and identify bottlenecks (power systems, cooling, facility constraints, and network capacity)
• Experience in influencing stakeholders and driving alignment across partner organizations without formal authority
• Hands-on capability with Python and SQL (or equivalent tooling) to build reproducible analysis and planning models
• Ability to define/validate assumptions, create “single source of truth” datasets/metrics, and productionize analyses through repeatable pipelines (in partnership with DS/DE where needed)
• Experience working with large, incomplete datasets and ambiguous inputs; ability to quantify uncertainty and clearly communicate the impact Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies