Energy Integration Manager

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.

Integrate energy data across the org by partnering with data and analytics teams to connect fragmented sources into a coherent, decision-grade portfolio picture — defining the shared data model, definitions, and source of truth that asset management, origination, and wholesale all rely on Up-level portfolio health visibility by taking reporting from periodic and manual to continuous, predictive, and trusted — surfacing risk, exposure, and opportunity early enough to act on, and setting the metrics, cadence, and review forums leadership runs the portfolio by Expand the operating model across all Energy teams by bringing a consistent, lightweight program operating system to data, analytics, asset management, energy origination, and wholesale — ensuring cross-team initiatives have clear ownership, dependencies are visible, and execution doesn't stall at the seams between functions Apply AI to scale the work by standing up AI and agentic tooling that automates portfolio reporting, flags anomalies and risks, drafts decision briefs, and compresses the time from data to insight to procurement action — serving as the org's pathfinder for where AI meaningfully accelerates energy operations Be a thought partner to all energy and partner teams by translating the integrated portfolio view into clear, executive-ready narratives that drive resourcing, prioritization, and procurement strategy for the years ahead

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