Data Center Central Planning Manager

Meta; Bellevue (WA); On-site; USD 180,000 - 260,000; Full time

Job summary

Meta is seeking a Data Center Central Planning Manager to lead constraint-aware capacity planning across power, cooling, space and network for its global data center fleet. You will translate product growth and AI workload evolution into actionable roadmaps and milestones.

In this role you will build quantitative models, partner with Capacity Engineering, Facilities, Network and Finance, and deliver clear recommendations to drive investments and execution in a fast-paced infrastructure

Qualifications

• Experience in data center capacity planning and infrastructure strategy.
• Ability to build and interpret quantitative models and scenario analyses.
• Experience working across multiple partner organizations without formal authority.
• Hands-on capability with Python and SQL to build reproducible analyses.

Responsibilities

• Drive portfolio capacity plans connecting demand signals to supply plans across MW, cooling, space, and network, with roadmaps.
• Build and maintain planning models (supply/demand, scenario analysis, sensitivities, bottleneck identification).
• Translate workload characteristics (GPU/AI clusters, rack power density, topology needs) into facility/electrical requirements.
• Assess system-level constraints and drive mitigation plans with responsible teams.
• Develop decision packages and narratives with clear trade-offs and assumptions.
• Partner with senior stakeholders, align pre-reads, surface risks, support planning governance.
• Drive cross-functional execution, track gaps, manage escalations, ensure model hygiene.

Skills

Data center capacity planning

Infrastructure strategy

Systems engineering

Python

SQL

Data analysis

AI/ML basics

Stakeholder influence

Education

Bachelor's degree in Computer Science/Engineering or equivalent

Bachelor’s degree in Engineering, CS, Applied Math, Physics, OR

Tools

Python

SQL

Job description

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)

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