Data Center Location Strategy Manager

Data Center Location Strategy Manager

Meta is seeking a Data Center Location Strategy Manager to lead the long-range site selection and geographic portfolio strategy that underpins Meta's global data center infrastructure. In this role, you will define where Meta builds its next generation of hyperscale data centers by evaluating power availability, land acquisition potential, network connectivity, regulatory environments, and community impact across global markets. You will serve as the authoritative voice on location strategy, shaping multi-year capital deployment decisions and influencing how Meta's infrastructure footprint evolves to support billions of users worldwide.

Responsibilities:

• Define and own Meta's multi-year global data center location strategy, including site identification, market prioritization, and portfolio optimization across owned and leased facilities.
• Lead comprehensive site feasibility assessments evaluating power grid capacity, utility infrastructure, land availability, fiber and network connectivity, seismic and climate risk, and total cost of ownership.
• Develop and maintain a global pipeline of prospective data center sites, coordinating with real estate, legal, policy, and infrastructure teams to advance sites through due diligence and entitlement.
• Build quantitative models and scenario analyses to compare site alternatives across financial, operational, and risk dimensions, translating findings into executive-level recommendations.
• Establish and refine the frameworks, tools, and methodologies used to evaluate and rank prospective data center markets globally.
• Represent location strategy in cross-functional planning forums, aligning site selection decisions with capacity planning, network architecture, and capital expenditure roadmaps.
• Drive commercial solutions through creative deal and option analysis and direct negotiations with external vendors and partners.
• Provide strategic guidance to leaders across infrastructure, finance, and policy on emerging market opportunities and risks affecting the global data center portfolio.
• Mentor other members of the capacity planning and site selection team, elevating analytical rigor and strategic thinking across the function.

Qualifications:

• Experience engaging with utilities, government agencies, and economic development bodies in the context of data center development or large infrastructure projects.
• Experience driving cross-functional alignment on long-range infrastructure strategy across engineering, finance, legal, and policy stakeholders.
• 12+ years of experience in data center site selection, infrastructure capacity planning, real estate strategy, or a closely related field within large-scale technology or energy infrastructure organizations.
• Experience leading multi-market site selection processes for hyperscale or large-scale data center facilities, including power, land, and network feasibility evaluation.
• Experience developing financial and operational models to support capital-intensive infrastructure investment decisions at an organizational or portfolio level.
• Bachelor's degree in a directly related field, or equivalent practical experience.
• Experience with renewable energy procurement strategies and power purchase agreement structures as applied to data center site selection.
• Familiarity with data center design standards, critical infrastructure requirements, and hyperscale construction processes that inform site suitability assessments.
• Experience building or scaling a location strategy function, including developing evaluation frameworks and decision-support tools used across an organization.
• Track record of influencing multi-billion-dollar capital deployment decisions through data-driven location analysis and executive-level communication.
• 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.