Meta
Data Foundations and Infrastructure Manager
$181K–$249K
Austin, TX +4 · Manager · Full-time · Posted Oct 12, 2026
- Location
- Austin, TX
Menlo Park, CA
Washington, DC
New York, NY
San Francisco, CA - Experience
- 12+ years of progressive experience in data analytics, data engineering, or a related quantitative field; 6+ years of people management experience.
- Education
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
About the job
Leads two teams building and modernizing workforce data infrastructure, automation tools, and AI agents that provide trusted insights for planning and executive decision-making.
What you'll do
- Lead and develop two pods spanning data engineering and AI, and set their technical direction and operating model.
- Modernize scalable data pipelines and self-service automation tools for reliable, AI-ready workforce data.
- Define and deliver AI-agent strategy, including deployment, evaluation, quality standards, and trusted workforce insights.
What they ask for
- 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 and shipping AI/ML-powered products, LLM-based agents, or automated analytics solutions from concept to production.
- Strong technical foundation in SQL, Python, and data pipeline orchestration (Airflow, Dataswarm, or equivalent).
Nice to have
- Familiarity with HRIS systems (Workday), enterprise data architecture, and HR data structures.
- Experience in People/HR analytics, workforce planning, or enterprise data domains.
Pay
$181,000/year to $249,000/year (base salary)
Tools
SQL, Python, Airflow, Dataswarm, Workday, LLMs, YAML
AI focus
Develop, evaluate, and productionalize AI skills and agents for workforce insights; build evaluation frameworks, AI quality standards, and data foundations optimized for agent consumption.
Model-written summary · the original posting is authoritative