Asset Management Operations Manager

Meta is seeking an experienced Asset Management Operations Manager to lead and optimize the lifecycle management of physical and IT assets across Meta's global infrastructure and site services operations. In this role, you will drive asset tracking, inventory control, audit execution, and disposition strategies that support Meta's data centers, offices, and technical facilities. You will partner with cross-functional teams including procurement, finance, IT, and facilities to ensure asset data integrity, compliance with internal controls, and operational efficiency across the full asset lifecycle from acquisition through retirement.

Responsibilities

Collaborate with cross-functional partners including Data Center Site Operations, Sourcing, Accounting, Finance, SOX compliance, Trade Compliance, and Tax to develop and oversee asset management business processes in a global environment Execute physical and logical audits of Meta's infrastructure asset base, conducting fieldwork including observations, assessments, and control testing to identify and document findings such as deficiencies and areas for improvement Domestic and International Travel is a requirement 25%-30% Identify and assess ongoing and emerging risks within asset management operations, developing approaches to resolve technical problems and building short- and long-term roadmaps aligned to business and operational needs Develop and maintain audit plans and checklists to ensure comprehensive asset coverage, and present findings with corrective recommendations to stakeholders Develop and improve decentralized policies, standards, processes, workflows, and controls to support emerging operational requirements with a focus on accountability, standardization, scale, and efficiency Conduct design reviews of proposed automation and process improvements against business goals, operations governance, data integrity, and compliance requirements Establish and monitor KPIs to measure asset management process performance and identify continuous improvement opportunities Maintain data integrity across multiple asset management and ERP systems, identify root causes of data inconsistencies, and leverage data insights to surface new operational requirements Develop process documentation for implementation and rollout of asset management business process improvements Support implementation of broader technology solutions and process automation to advance asset lifecycle tracking and inventory reconciliation at scale

Qualifications

6+ years of experience in asset management, supply chain operations, or technical logistics within large-scale IT or facilities environments Experience managing physical and IT asset lifecycles including tagging, tracking, auditing, and disposition processes Experience with enterprise asset management or ERP systems such as ServiceNow, Oracle, or SAP for inventory tracking and financial reconciliation Experience developing and enforcing inventory control procedures and supporting Sarbanes-Oxley or equivalent compliance requirements Experience using data analysis tools to monitor asset inventory metrics, identify discrepancies, and drive operational decisions Experience integrating AI tools or automation into asset tracking and inventory reconciliation workflows to drive measurable efficiency gains Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Familiarity with IT asset disposition standards, including data sanitization, remarketing, and environmentally responsible recycling practices Experience managing asset operations across data center or large-scale technical site environments, including third-party logistics or asset management vendor oversight IAITAM or ITIL certification, or equivalent professional credential in asset or service management Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)