Engineering Principal Tech Lead Manager (tlm) – Snowflake Feature Store

Snowflake Snowflake · Data AI · WA-Bellevue, United States · Engineering

Engineering Manager to lead a team building distributed systems for Snowflake's Feature Store, focusing on real-time and online serving infrastructure for enterprise ML workloads. The role involves leading a team, driving system design, shaping architecture for streaming pipelines, and ensuring reliable, low-latency feature access for AI applications.

What you'd actually do

  1. Lead and grow a team responsible for online feature serving and real-time feature infrastructure.
  2. Drive the design and delivery of distributed systems supporting low-latency, high-throughput feature access for inference workloads.
  3. Shape architecture for streaming pipelines and stateful processing systems that ensure freshness and consistency of ML features.
  4. Own execution of customer-facing features from design through production rollout.
  5. Partner cross-functionally to translate enterprise ML requirements into scalable technical solutions.

Skills

Required

  • 8+ years of experience building distributed systems, data infrastructure, or backend platform services.
  • 3+ years of engineering management experience leading high-performing teams.
  • Strong background in distributed systems fundamentals, including scalability, fault tolerance, consistency, and performance tuning.
  • Experience building or operating low-latency, real-time, or online serving systems.
  • Experience with large-scale data infrastructure and streaming systems.
  • Demonstrated track record of shipping customer-facing platform products at scale.
  • Experience operating highly available, multi-tenant cloud services.
  • Strong communication skills and the ability to collaborate across engineering and product teams.

Nice to have

  • Exposure to machine learning systems, feature engineering workflows, or model serving infrastructure.

What the JD emphasized

  • low-latency
  • real-time
  • enterprise scale
  • low-latency
  • real-time
  • online serving systems
  • large-scale data infrastructure
  • streaming systems
  • customer-facing platform products at scale
  • highly available, multi-tenant cloud services
  • low latency
  • high reliability

Other signals

  • Leading a team building distributed systems for feature computation, storage, and low-latency serving.
  • Working at the intersection of large-scale data infrastructure and real-time ML systems.
  • Enabling production ML workloads for global enterprises.