Area Schedule Lead - Data Center Design, Engineering and Construction

Meta Meta · Big Tech · San Francisco, CA +1 · Remote

This role is for an Area Schedule Lead in Meta's Data Center Design, Engineering, and Construction division. The primary focus is on technical schedule management and reporting for a portfolio of data center projects, aiming to optimize speed to market and mitigate schedule risks. While the role involves integrating and developing skills in AI tools for workflow optimization and ethical AI practices, the core function is project scheduling and construction management, not AI/ML development.

What you'd actually do

  1. Responsible for end to end schedule coordination and updates, including interface with risk management and pro-active communication of updates and alignment of variance root cause/commentary with Cross-functional partners
  2. Accountable for all aspects of vendor and Contractor schedule management
  3. Will lead efforts to optimize schedules for speed to market and successful on-time-delivery
  4. Responsible for making recommendations that are in line with contract and escalating when site decisions differ from the contractual or program guidance
  5. Lead the identification and application of Proactive Risk Indicators in sub-area and ensure all risks are properly escalated to Health Reviews, and other appropriate forums

Skills

Required

  • Primavera P6
  • Construction Management
  • Data Center Infrastructure
  • Risk Management
  • Contract Management
  • AI tools integration
  • Ethical AI practices
  • AI skill development

Nice to have

  • Prompt/context engineering
  • Agent orchestration

What the JD emphasized

  • 10+ years of Planning, Scheduling, Construction Management, or Related field experience
  • Bachelor’s degree in Engineering, Construction Management, or Equivalent Technical Field or related field experience
  • Subject Matter Expert in Primavera P6 and other scheduling related methodologies and software
  • 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