About the Team Data Foundations & Infrastructure (Data Foundations & Infrastructure) is a newly established team within Workforce Strategy & Insights (Workforce Strategy & Insights), responsible for owning and delivering data foundations and scalable support for reporting, analytics, and AI-readiness across WSI and for People@ at scale. We partner closely with Workforce Planning and Headcount Management teams, People Analytics (workforce insights and engagement), and enterprise stakeholders to ensure that workforce data powers executive decision-making across Meta. This role will lead two critical pods within DFI: AI Enablement: Develop, evaluate, and productionalize AI agents that scale analyst impact and deliver trusted, intelligent insights across workforce management domains. Data Infrastructure and Automation Tools: Build, maintain, and modernize scalable data pipelines, automation tooling, and access management to revamp operations for efficiency and AI readiness. The Role We are looking for a Senior Analytics Manager to lead the data foundations and AI capabilities that workforce intelligence at Meta runs on - owning both the pipelines and automation tooling that produce our data and the AI agents that consume it. This is a leadership role at the intersection of data engineering, AI productionalization, and workforce analytics - reporting directly to the Director of DFI. You will manage two established pods, each with an experienced pod lead, and own the strategy that connects them: building pipelines and automation optimized for agent consumption, and building the agents that turn them into trusted insights. You'll set technical direction and operating model, modernize legacy pipelines and consolidate self-service tooling, and partner deeply across People, Infrastructure & Engineering (PIE), Data Governance, Workforce Management, People Analytics, and Finance. This is a net-new role with significant latitude to shape the operating model and technical direction of both pods - with direct visibility to VP+ leadership and company-wide impact on how Meta plans and manages its 70,000+ person workforce.
Responsibilities
Lead and develop two pods (8 people, including two pod leads) spanning data engineering and AI development, promoting technical excellence, speed, and AI-native thinking
Define and execute the strategic roadmap for data infrastructure modernization - migrating and revamping legacy pipelines into scalable, automated systems optimized for AI consumption
Own the end-to-end AI enablement strategy: building, evaluating, and deploying AI skills, agents, and evaluation frameworks that deliver trusted workforce insights at scale
Uphold and advance 4Agent vs. Analyst4 parity - ensuring agents reach the same conclusions an analyst would, in a manner that is reconcilable and evaluable
Own the reliability layer for AI readiness: pipelines built to agent-appropriate standards for latency, format, and refresh cadence, with data quality and compliance guarantees behind every agent
Drive the development and consolidation of self-service automation tools with ongoing monitoring and continuous improvement
Partner with domain owners to define guidelines for core versus bespoke skill creation
Implement product enhancements, including Context Library, Metric Governance, YAML configurations, and semantic layer builds, to drive AI agent accuracy
Establish and maintain operating models for ownership of tables, pipelines, access control lists, skills, evaluations, and agents across DFI, WSI, and XFN partners
Partner with Data Governance on metric and AI-skill certification, and ensure compliance with People data classification standards
Collaborate with Workforce Management, People Analytics, Finance, and Recruiting leadership to ensure our data foundations support workforce planning, headcount management, and executive reporting needs
Maintain technical depth sufficient to evaluate the direction, critically evaluate the pipeline and agent design, and hold credibility with your teams
Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience
Experience influencing and partnering with executive stakeholders (VP+) across technical and business functions
Experience defining measurable milestones and communicating delivery progress and business impact to non-technical stakeholders
12+ years of progressive experience in data analytics, data engineering, or a related quantitative field
6+ years of people management experience, including managing senior ICs or team leads across data engineering and/or analytics teams
Demonstrated experience leading AI-native transformations - shipping AI/ML-powered products, LLM-based agents, or automated analytics solutions from concept to production
Hands-on experience with generative AI tools and large language models (LLMs) for analytics, reporting, and automation
Experience building evaluation frameworks and quality standards for AI/ML outputs
Strong technical foundation in SQL, Python, and data pipeline orchestration (Airflow, Dataswarm, or equivalent)
Track record of defining and executing multi-quarter technical roadmaps for data infrastructure modernization at scale
Experience building or modernizing pipelines and self-service automation tooling used by analysts and downstream products
Experience managing data governance, access controls, and compliance for sensitive data domains
Preferred Qualifications
Familiarity with HRIS systems (Workday), enterprise data architecture, and HR data structures
Background building or managing AI agent systems, including skills development, evaluation pipelines, and productionalization workflows
Experience designing metric semantics, context libraries, or semantic layers that both human-facing products and AI agents consume
Experience operating amid ambiguity and in-flight roadmaps, converting it into decision frameworks
Demonstrated track record of mentoring and developing technical talent across career stages
Experience preparing data platforms for agentic consumption - metadata quality, lineage, and certification at scale
Experience at a large-scale technology company managing analytics or data engineering teams supporting corporate functions
Experience in People/HR analytics, workforce planning, or enterprise data domains
Graduate degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field
