Meta is seeking a Data Center Capacity Engineer to support the planning, analysis, and optimization of server and infrastructure capacity across our global data center fleet. In this role, you will work at the intersection of site operations and capacity planning, translating demand forecasts and utilization data into actionable deployment strategies that ensure Meta's data centers can reliably support billions of users. You will collaborate with infrastructure, network, and operations teams to align capacity supply with business demand, identify bottlenecks, and drive efficient use of compute, storage, and power resources across large-scale data center environments.

Responsibilities

Overall accountability for capacity workflows (receiving, moves, decommissions), facilitating collaboration among various cross-functional partners to meet capacity demands
• Collaborate closely with key stakeholders and partners to implement strategies and drive initiatives that lead to meaningful improvements in support of data center operations. Maintain consistent touchpoints with key XFN partners across data centers
• Analyze business capacity demands and translate that data into local plans to enable rapid delivery of capacity to the site
• Plan, lead and collaborate with cross-functional data center teams to deliver complex data center infrastructure capacity projects in support of Meta's growth, considering the interdependencies of production resiliency, power, cooling, network, server and application layers
• Build cross-functional relationships and have the ability to influence policies and procedures to improve regional/global data center operations
• Implement and share best practices across all global data centers for all elements of capacity while establishing standard practices for innovation, collaboration, accountability, continuous improvement, and safety
• Drive alignment and execution of key capacity, engineering, and operational initiatives across functional partners at the site. Ensure operational consistency, to scale operations efficiently and effectively
• Lead data analytics, metrics, and interpretation of a complex environment to identify inefficiencies, opportunities, exceptions, and correlations, and proactively respond before they impact data center uptime and utilization. Perform root cause analysis of complex technical and engineering issues and drive resolution
• Create/improve global standards for processes, workflows, and automation roadmaps for software automation that facilitate deployment, maintaining, and decommissioning of server hardware at scale
• Work with Meta hardware and software engineering teams to help resolve complex technical issues that affect Meta's computing infrastructure
• Share knowledge with capacity team members, both locally and globally. Seek out and provide guidance on challenges others are having and actively fix them in a scalable way
• Display knowledge of infrastructure (including, but not limited to, cooling, power, networking) as it relates to the capacity role

Minimum Qualifications
• 5+ years of experience in a combination of capacity planning, demand and supply management, production planning, operations planning, or infrastructure management
• BS, BA, or BEng in a relevant field, or equivalent experience or certification
• Experience in process ownership and development, and in systems development
• Knowledge of enterprise-level networking, server, and storage installs
• Comprehensive understanding of data center infrastructure systems and applications, as well as technical knowledge from related industries such as pharmaceuticals, nuclear, or large-scale manufacturing
• Ability to communicate effectively, in a clear and concise manner, and appropriately tailor messages to the audience
• Demonstrated ability to solve complex problems and to think at scale

Preferred Qualifications
• SQL, Python, or other programming and automation experience
• Project management and delivery experience. Certifications in Agile or PMP certification
• Experience in the application of data-driven continuous improvement through lean Six Sigma data and process analysis, visualization, and data modeling