Engineering Manager - Feature Store

Snowflake•Menlo Park, CA

51d

About The Position

Snowflake’s Feature Store is a core component of our Machine Learning platform, enabling customers to build, manage, and serve machine learning features directly within the Snowflake Data Cloud. It powers both offline training and low-latency online inference, supporting batch and streaming pipelines at enterprise scale while maintaining Snowflake’s standards for governance, reliability, and performance. We are looking for a highly technical Engineering Manager to lead the development of real-time and online serving infrastructure that enables production ML workloads for global enterprises. As an Engineering Manager on the Feature Store team, you will lead a team building distributed systems that power feature computation, storage, and low-latency serving. You will work at the intersection of large-scale data infrastructure and real-time ML systems, ensuring that features are computed reliably and served consistently between training and inference workflows. You will partner closely with Product, Snowpark, ML Platform, and core infrastructure teams to deliver customer-facing capabilities that support mission-critical AI applications. This role requires strong technical depth in distributed systems and real-time data platforms, combined with a proven ability to lead teams and ship high-quality products.

Requirements

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

Responsibilities

Lead and grow a team responsible for online feature serving and real-time feature infrastructure.
Drive the design and delivery of distributed systems supporting low-latency, high-throughput feature access for inference workloads.
Shape architecture for streaming pipelines and stateful processing systems that ensure freshness and consistency of ML features.
Own execution of customer-facing features from design through production rollout.
Partner cross-functionally to translate enterprise ML requirements into scalable technical solutions.
Establish strong engineering practices around performance optimization, observability, reliability, and operational excellence.
Contribute hands-on to architecture reviews and critical technical decisions.