Data Engineering Manager, Analytics

Meta Meta · Big Tech · Menlo Park, CA +1 · Remote

Meta is seeking a Data Engineering Manager to lead a team specializing in AI/ML solutions for operations. The role involves overseeing the development and deployment of AI/ML models to automate and optimize global operations, focusing on scalable backends for post-monitoring and measurable cost reduction. The manager will partner with various teams to identify opportunities, build partnerships, and drive end-to-end project delivery, establishing best practices in data engineering and model development.

What you'd actually do

  1. Build, mentor, and manage a high-performing data engineering team specializing in AI/ML solutions for operations.
  2. Lead governance, adoption and impact measurement of advanced AI/ML models to automate and optimize global operations workflows by partnering with ENG and DS teams.
  3. Oversee the creation of scalable, analytics-driven backends to enable effective post-monitoring of AI deployment and measurable OpEx reduction.
  4. Partner with Operations, Product, Engineering, Data Science teams to identify and prioritize high-impact opportunities for AI-driven automation.
  5. Drive end-to-end project delivery, from ideation and prototyping to production deployment and ongoing optimization.

Skills

Required

  • Data architecture
  • ETL pipelines
  • Distributed systems
  • AI/ML model development and deployment
  • Influencing cross-functional teams
  • Coaching, mentoring, and developing high-performing teams
  • Project management
  • Agile methodologies

Nice to have

  • AI/ML solutions for operations
  • advanced AI/ML models
  • scalable, analytics-driven backends
  • post-monitoring of AI deployment
  • measurable OpEx reduction
  • AI-driven automation
  • impact measurement of AI models
  • operational performance
  • cost savings
  • senior leadership communication
  • technical outcomes into business value
  • ideation
  • prototyping
  • production deployment
  • ongoing optimization
  • data engineering best practices
  • model development best practices
  • scalability
  • reliability
  • compliance
  • industry trends in AI, data engineering, and operations technology
  • process improvement
  • innovation

What the JD emphasized

  • AI/ML solutions for operations
  • AI/ML models to automate and optimize global operations workflows
  • AI deployment
  • AI-driven automation
  • AI models on operational performance
  • data architecture, ETL pipelines, distributed systems, and AI/ML model development and deployment

Other signals

  • AI/ML solutions for operations
  • AI/ML models to automate and optimize global operations workflows
  • AI deployment
  • AI-driven automation
  • AI models on operational performance
  • AI, data engineering