Capacity Engineer, Infrastructure Data Governance

Meta is seeking a Data Governance engineer to help build and operate data governance across infrastructure planning and supply chain systems. This role will implement 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 turn defined governance needs into technical requirements, project plans, milestones, and measurable outcomes.

This role is 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. The right candidate brings experience making technical decisions informed by data, system constraints, and stakeholder requirements, consistent delivery of scoped projects on time and within defined quality standards, experience communicating project status, risks, and decisions in writing and in meetings to technical and non-technical stakeholders, and the ability to work effectively with partner teams.

Responsibilities

Execute governance processes for critical infrastructure data including intake, ownership, definitions, quality expectations, review cadences, escalation paths, change management, and compliance monitoring
Analyze business, manufacturing, and logistics data to identify governance gaps, source-of-truth conflicts, and data defects that degrade supply chain and capacity decisions
Build and maintain operating mechanisms and tooling that support data ownership, stewardship responsibilities, source-of-truth expectations, approval flows, audit trails, and exception handling
Support cross-team adoption of governance standards and partner with teams to meet agreed processes and quality bars
Contribute to end to end supply chain processes, methodologies, and data to deliver an executable supply plan grounded in trustworthy inputs
Triage and resolve data escalations and exceptions in partnership with owning teams
Track dependencies, risks, rollout criteria, adoption blockers, and success indicators for governance workstreams
Work cross-functionally to define problem statements, collect data, build analytical models, and make recommendations
Communicate project status, risks, decisions, and compliance health clearly to technical teams and cross-functional partners
Create and maintain high-quality artifacts, including project plans, ownership models, dependency trackers, risk and mitigation plans, rollout plans, compliance dashboards, and status updates
Improve how Meta executes infrastructure data governance by simplifying processes and scaling repeatable patterns

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
5+ years of experience in operations research, industrial engineering, supply chain management, systems engineering, technical program management, or related field
Experience with data quality, data management, or data governance practices such as ownership models, standards, controls, or compliance tracking
Experience delivering technical projects across more than one team 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
Experience defining metrics, dashboards, or review mechanisms for technical programs
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience in infrastructure operations and technical infrastructure knowledge
Experience working with cross-functional teams
Experience communicating and translating technical and non-technical requirements for cross-functional stakeholders
2+ years of experience in coding/scripting languages such as Python, R, Java, C, C++, PHP
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience in infrastructure, supply chain, data center, networking, capacity planning, logistics, or finance operational domains
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
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 working with partner teams across engineering, planning, supply chain, operations, analytics, and finance
Experience questioning the norm and improving existing business systems and processes
Masters degree in Operations Research, Industrial Engineering, Computer Science, or related technical field
Familiarity with master data management practices, data quality frameworks, metric definitions, lineage, or stewardship models
Experience working with distributed systems at scale
Experience defining structure and next steps in loosely scoped problem spaces, and independently acquiring new domain knowledge to deliver results
7+ years of technical, manufacturing operations, or technical project management experience in the hardware systems or infrastructure industry