Job description

Meta is seeking a Data Governance leader to establish and lead data governance across infrastructure planning and supply chain organizations and systems. This role will define how critical infrastructure data is governed end to end, from intake and ownership through quality, controls, and adoption, so teams can make high-quality decisions, operate efficiently, and execute reliably at Meta scale. We will do this primarily by building software, mathematical models, and automated tracking of key performance metrics that make data quality, lineage, and control health measurable rather than aspirational. You will translate ambiguous governance needs into clear technical requirements, operating models, roadmaps, milestones, and measurable outcomes. This role is very cross-functional and provides an opportunity to work with supply chain partners, infrastructure engineering, planning, data center, networking, data science, analytics, finance, and operations teams to power the rapid growth of Meta's products. This is a senior individual-contributor role. The right candidate brings deep technical judgment, structured program leadership, crisp written and verbal communication, and the ability to influence senior leaders across multiple infrastructure organizations without relying on authority. Own the end-to-end governance process for critical infrastructure data including intake, ownership, definitions, quality expectations, review cadences, decision rights, escalation paths, change management, and compliance monitoring. Develop and analyze business, manufacturing, and logistics data to identify governance gaps, source-of-truth conflicts, and data defects that degrade supply chain and capacity decisions. Establish durable operating mechanisms that clarify data ownership, stewardship responsibilities, source-of-truth expectations, approval flows, audit trails, exception handling, and accountability across teams. Drive cross-team adoption of governance standards and work with partner teams to ensure compliance with agreed internal processes, quality bars, and operating rhythms. Contribute to end to end supply chain processes, methodologies, and data to deliver an executable and optimized supply plan grounded in trustworthy inputs. Manage and resolve critical escalations and data exceptions across all areas of the supply chain. Identify technical dependencies, risks, sequencing constraints, rollout criteria, adoption blockers, and measurable success indicators across multiple infrastructure organizations. Work cross-functionally to define problem statements, collect data, build analytical models, and make recommendations that drive change at the most strategic levels. Communicate program strategy, status, risks, decisions, compliance health, and impact clearly to technical teams, cross-functional partners, and executive stakeholders. Create and maintain high-quality artifacts, including governance charters, technical program plans, cross-org roadmaps, ownership and RACI models, dependency trackers, risk and mitigation plans, rollout plans, compliance dashboards, and executive updates. Improve how Meta executes infrastructure data governance by simplifying processes, scaling repeatable patterns, and mentoring senior ICs and cross-functional partners. Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 10+ years of experience in operations research, industrial engineering, supply chain management, systems engineering, technical program leadership, or related field Experience leading data governance or data management programs, including establishing end-to-end governance processes, ownership models, standards, controls, adoption mechanisms, and compliance tracking Experience delivering technical programs or products from inception to launch across multiple teams or organizations Experience communicating and translating technical and non-technical requirements for cross-functional stakeholders Experience working with cross-functional teams Experience managing ambiguity, with a drive for continuous learning Masters or Ph.D. degree in Operations Research, Industrial Engineering, Computer Science, or related technical field 3+ years of experience in coding/scripting languages such as Python, R, Java, C, C++, PHP 12+ years of technical, manufacturing operations, or technical program leadership experience in the hardware systems or infrastructure industry Experience leading data governance programs in infrastructure, supply chain, data center, networking, capacity planning, logistics, finance, or similarly complex operational domains Technical understanding of infrastructure data domains, such as demand planning, capacity planning, inventory, sourcing, procurement, logistics, deployment, data center operations, networking, asset lifecycle, or service capacity Experience defining source-of-truth strategy, master data management practices, data quality frameworks, metric definitions, lineage, stewardship models, or data operating models Experience working with distributed systems at scale Experience in infrastructure operations and technical infrastructure knowledge Experience defining metrics, control health indicators, adoption measures, compliance dashboards, or review mechanisms for technical governance programs Experience transforming business systems and models and achieving results relative to goals, including questioning the norm and thinking out-of-bounds Proven experience interfacing with and establishing relationships with management at suppliers or customers Experience influencing senior leaders across infrastructure, engineering, planning, supply chain, data center, networking, operations, analytics, finance, and business organizations Experience driving programs across centralized platforms and federated business or infrastructure teams Experience mentoring senior ICs and raising the quality of program execution across an 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) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Requirements

Requirements not extracted — see the original description below.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonjavaai

Platform: meta
Effort: medium