Supply Chain Operations Lead, Infrastructure Supply Chain

Meta; Bellevue, Austin, Menlo Park (WA, TX, CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Meta is seeking a seasoned professional to drive strategic operations for its Infrastructure Supply Chain & Engineering team. You will identify risks, optimize processes, and influence stakeholders to keep our supply chain resilient and efficient.

The role emphasizes data-driven decision making, collaboration with cross-functional partners, and applying AI tools to scale AI-enabled infrastructure across Meta's global operations.

Qualifications

• 8+ years in business strategy, operations, or related field within supply chain
• Bachelor's degree in business or related field
• Proficiency in data analysis, problem-solving, and data-driven strategies
• Effective communication with stakeholders at all levels
• Experience leading teams, managing projects, and influencing stakeholders
• Experience building relationships with legal, policy, finance and cross-functional partners
• Demonstrated critical thinking, analyzing complex situations, and developing strategies
• Experience using data analysis to define and solve cross-functional operational challenges
• Demonstrated ongoing AI skill development (e.g., prompt engineering, agent orchestration) and staying current with AI technologies
• Experience with technical concepts relevant to supply chain operations
• Experience supporting large-scale supply chain organizations
• Ability to integrate AI tools to optimize workflows and drive measurable impact
• Experience implementing responsible, ethical AI practices (risk assessment, bias mitigation)

Skills

Data analysis

Stakeholder communication

Strategic operations

Cross-functional leadership

AI tools integration

Problem solving

Supply chain basics

Influence & negotiation

Education

Bachelor's degree in business

Job description

Meta is seeking a professional experienced in driving strategic operations for large-scale supply chain organizations to execute on projects critical to Meta's Infrastructure Supply Chain & Engineering team. This role involves leveraging functional expertise, thought leadership and strategic influence to help identify and solution risks that may compromise or adversely impact our supply chain, optimize processes and drive efficiency. Meta’s infrastructure supply chain has global scale, complexity and the opportunity to tackle unique challenges and drive creative solutions. If you like an impact focused environment where you will be essential to enable Meta’s unrivaled AI-enabled infrastructure, this role may be a strong fit!

We bring industry-leading data center infrastructure and computing systems to power the needs of Meta’s global community of 3B+ users and accelerate the future of AI.

• 8+ years of experience in business strategy, operations, or a related field, in supply chain role
• A Bachelor's degree in business or a related field
• Proficiency in data analysis, problem-solving, and developing data-driven strategies
• Effective communication skills with stakeholders at all levels
• Experience in leading teams, managing projects, and influencing stakeholders
• Experience building relationships with legal, policy, finance, and other cross-functional partners and driving discussions around strategic projects
• Demonstrated experience of thinking critically, analyzing complex situations, and developing effective strategies
• Experience using data analysis to define, scope, and recommend solutions for ambiguous cross-functional operational challenges
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience with technical concepts relevant to supply chain operations
• Experience supporting large scale supply chain organization
• 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)