Manager, Data Engineering – Commerce

Netflix Netflix · Big Tech · United States · Remote · Data & Insights

Manager for a Data Engineering team at Netflix focused on optimizing the member acquisition and commerce experience. The team is responsible for foundational data products that drive sign-up, payment processing, and core commerce flows, supporting experimentation and insights for Product Management and Data Science. The role involves team leadership, engineering enablement (tooling, frameworks, best practices), and platform strategy partnership.

What you'd actually do

  1. Lead and Inspire: Hire, coach, and grow a diverse, high-performing team of data and software engineers, fostering their technical and professional development through direct, constructive, and empathetic feedback.
  2. Define Vision: Develop and execute a clear, impact-oriented roadmap for the team, aligning both core data initiatives and engineering enablement goals with broader Commerce and company objectives.
  3. Drive Execution: Oversee the design, building, and scaling of robust, well-modeled, and reliable data products that support experimentation, analytics, and machine learning across the commerce domain.
  4. Build Tooling & Frameworks: Lead a critical function within the team dedicated to building reusable data frameworks, development tooling, and automation capabilities that significantly increase the productivity, efficiency, and data quality of the entire Commerce Product Data Engineering organization.
  5. Operational Excellence: Define and drive best practices for data modeling, pipeline architecture, testing, and observability, ensuring a high bar for engineering excellence across the team.

Skills

Required

  • Data Engineering
  • Distributed Systems
  • Data Modeling
  • Large-scale data processing (Spark, Flink or similar)
  • Engineering Leadership
  • Team Management
  • Stakeholder Management
  • Product Mindset

Nice to have

  • Commerce/Acquisition Domain Knowledge

What the JD emphasized

  • 5+ years of experience in engineering leadership, managing and growing high-performing Data Engineering teams
  • strong background in data engineering, distributed systems, data modeling, and large-scale data processing technologies
  • specific, demonstrable experience leading efforts to build shared tooling, infrastructure, or platform features that enabled other data engineering teams to move faster and more reliably