Data Center Central Planning Manager

Meta•San Francisco, CA

10h•$184,000 - $257,000

About The Position

Meta is seeking a Data Center Central Planning Manager to join its Data Center Strategic Planning organization. The role develops constraint-aware data center capacity plans that translate product growth, AI workload evolution, and fleet architecture into actionable roadmaps for power, cooling, space, and network capacity. It combines capacity engineering, portfolio planning, and investment decision-making, requiring quantitative analysis and scenario development to inform infrastructure choices. The manager works across Capacity Engineering, Facilities, Network, Construction, Site Selection, Finance, and Operations, and communicates recommendations that help advance decisions.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, Engineering, Applied Math, Physics, Operations Research, or equivalent practical experience. The posting lists bachelor's degree qualifications in two separate statements.
8+ years of experience in data center, infrastructure, cloud, or 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 through decision and execution, creating structure and making progress through partners.
Strong quantitative background, including building models, performing scenario and sensitivity analyses, and making recommendations under uncertainty.
Technical fluency across data center domains sufficient to drive trade-offs and identify bottlenecks in power systems, cooling, facility constraints, and network capacity.
Experience influencing stakeholders and driving alignment across partner organizations without formal authority.
Hands-on capability with Python and SQL, or equivalent tooling, to build reproducible analyses and planning models.
Ability to define and validate assumptions, create single-source-of-truth datasets and metrics, and productionize analyses through repeatable pipelines, in partnership with data science or data engineering as needed.
Experience working with large, incomplete datasets and ambiguous inputs; ability to quantify uncertainty and clearly communicate its impact.

Nice To Haves

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

Responsibilities

Drive portfolio capacity plans connecting product and AI demand signals to supply plans and execution paths across megawatt delivery, cooling envelope, space readiness, and network capacity, including near- to mid-term roadmaps and readiness milestones.
Build and maintain quantitative planning models for supply and demand, scenario analysis, sensitivities, bottleneck identification, schedule risk, and uncertainty to support prioritization and sequencing decisions.
Translate workload characteristics—including GPU/AI clusters, rack power density, resiliency tiers, topology and interconnect needs, and placement constraints—into facility and electrical requirements, megawatt ramp profiles, readiness gates, and deployment constraints.
Assess system-level constraints across the fleet and pipeline, including utility delivery, substation and transformer capacity, cooling limits, heat rejection, building envelopes, network ingress and egress, and construction sequencing; develop mitigation plans with responsible teams.
Develop decision packages and narratives that synthesize analysis into clear trade-offs and recommendations on cost, schedule, reliability, operability, and risk, while explicitly identifying assumptions and sensitivities.
Partner with senior stakeholders by aligning pre-reads, surfacing key risks early, and supporting planning decisions while maintaining technical credibility with engineering partners.
Drive cross-functional execution by aligning dependencies, tracking gap closure, managing escalations as needed, and supporting planning governance through cadence, documentation, and model hygiene.