Energy Integration Manager

Meta; Denver, Northern (CO, KY); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Meta seeks a program leader to unify data, decisions, and AI-powered insights across the energy portfolio in Denver, CO. The role focuses on connecting analytics, origination, and procurement through trusted reporting and governance.

Demonstrated ability to scale AI solutions, coordinate multiple teams, and communicate clearly with executives is essential for driving faster, more informed energy decisions at scale.

Qualifications

• Hands-on AI/automation experience in operational or analytical work.
• Experience leading complex cross-functional programs or operations in energy/infra contexts.
• Ability to distill complex data into leadership-ready narratives.

Responsibilities

• Own how data, decisions, and execution come together into a single AI-accelerated view of portfolio health.
• Lead cross-functional teams to turn signals into procurement and origination strategies.
• Establish operating cadences, governance, and reporting used by leadership.

Skills

Data fluency

AI/automation experience

Cross-functional leadership

Executive communication

Portfolio health reporting

Governance

Prompt/context engineering

Agent orchestration

Job description

Meta's energy footprint is scaling faster than any single team can assess end to end. We have deep specialist strength across data, analytics, asset management, and energy origination - but the connective tissue between them, the shared view of portfolio health, and the operating cadence that turns those signals into procurement decisions are still maturing. This role builds the connective tissue.

This is a force‑multiplier role for the entire Energy organization: one person owning how our teams' data, decisions, and execution come together into a single, trusted, AI‑accelerated view of portfolio health - and using it to unlock faster, sharper, more confident energy procurement at scale. It is explicitly complementary to our specialist teams: it does not replace their judgment or own their functions; it makes each of them faster, more connected, and more visible to leadership. Demonstrated experience standing up operating cadences, portfolio/health reporting, and governance that leaders actually run their business by Proven track record of leading complex, cross‑functional programs or operations in energy and infrastructure Hands‑on experience applying AI/automation to operational or analytical work, with sound judgment on where it adds leverage versus where it does not Strong data fluency - comfortable defining metrics and data models, working directly with analytics teams, and turning messy multi‑source data into trusted decision‑grade reporting Executive communication skills with the ability to distill complexity into crisp narratives for leadership and influence without authority across specialist teams 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 Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Background spanning both technical (data/analytics) and commercial (procurement/origination/wholesale) contexts 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 Domain knowledge of large energy portfolio metrics, energy markets, procurement, and the energy origination process Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and familiarity with responsible AI practices including risk assessment and quality reviews Experience driving alignment and operational cohesion across multiple teams by establishing shared processes, visibility, and cross‑functional coordination Experience building AI/agentic workflows in a production setting (e.g., automated reporting, anomaly detection, decision‑support tooling) with demonstrated ability to optimize/redesign workflows and drive measurable impact