Director, Data Engineering

Meta•Menlo Park, CA

About The Position

You'll be one of the analytics leaders for how Meta transforms its Enterprise through AI. This is a 0→1 role with enterprise-wide scope, high ambiguity, and direct visibility to leadership. If you want to shape how 70,000+ people work—and measure whether it's actually working—this is the job. We are open to hiring Data Science or Data Engineering profiles. In our world, both are Analytics. The work will span the full spectrum—from building the data infrastructure that powers enterprise measurements to shaping the strategic frameworks that define what "good" looks like. Your title matters less than your demonstrated experience to operate across that range. THE TEAM 2026 is a step-function year for AI at Meta. We're not just building AI products for the world—we're fundamentally rewiring how we work internally. This is driven by AI4W (AI for Work), a company-wide effort to integrate AI into every tool, team, and process at Meta. This role reports to Enterprise Analytics leaders and will be one of the experienced ICs on a new “Ecosystem" analytics team. You'll sit at the intersection of the teams actually building Meta's internal AI future (Metamate, Devmate, Analytics Agent, vibe coding platforms) and Enterprise Engineering (EE), which manages 680+ products and 6 of Meta's top 10 internal tools, powering everything from recruiting and financial planning to supply chain operations and employee support. Across EE, we're watching the real-time pivot from "passive AI assistance" to autonomous agents that don't just advise but execute: sourcing candidates, calculating tax provisions, resolving IT tickets, and accelerating analytics workflows through cookbooks, semantic models, and self-serve recipe systems. There is no playbook and the measurement frameworks are nascent.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
AI power user. You're already a power AI user in your day-to-day work—Metamate, Claude, Cursor, or whatever tools make you faster. You'll set an example for what "AI-native" looks like and help others get there
0→1 builder experience You've built measurement systems from scratch in ambiguous spaces. You don't wait for requirements—you define them
Speed + rigor. You can move fast without being sloppy. You know when to be 60% right now vs. 95% right later
Executive communication. You'll regularly present to leadership. Your insights need to be crisp, actionable, and defensible
Cross-functional influence. You'll work across dozens of teams (EE, Security, CPP, DevInfra, and more). You need to drive alignment without authority
Honesty. Some AI initiatives won't work. Some claimed impact will be inflated. You'll need to call it like you perceive it

Nice To Haves

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 with productivity/efficiency measurement, internal tools, or enterprise products
Familiarity with LLMs, agentic systems, or AI tooling
Prior experience in a founding/early team member role

Responsibilities

Leadership asks "what's the ROI of [new AI tool]?" on Wednesday. You have an answer by Friday
A team claims their AI initiative saved 10,000 hours. You validate (or invalidate) it
You rapidly instrument, measure, and communicate whether it's working
You jump into whatever is urgent and ambiguous—and you close it
Build measurement frameworks that work across wildly different AI tools and use cases (coding, analytics, recruiting, HR support, supply chain, finance and more)
Create the dashboards, workspaces, semantic models and self-serve layers that let stakeholders across the company understand progress without pinging you
Design and scale the data pipelines and instrumentation that capture agent telemetry, usage signals, and outcome metrics across a fragmented and fast-moving tool landscape
Shape the strategy for how we think about productivity, time savings, and quality improvements in an AI-augmented workforce
Influence how Analytics (and business functions) evolve their operating models, job profiles, and organization structures for the AI era