Engineering Manager - Feature Store

Snowflake2 weeks ago

Location

US-WA-Bellevue

Type

Full Time

Salary

USD 236,000 – 339,200

Level

Manager

Role

Engineering Manager

Posted

Feb 23, 2026

Full TimeManager

The role

Summary

Snowflake is seeking an Engineering Manager for their Feature Store team to lead the development of real-time ML infrastructure and online serving systems. This role requires deep technical expertise in distributed systems and 3+ years of engineering management experience, focusing on building low-latency feature serving capabilities for enterprise AI applications.

What you'll do

Team Leadership: Lead and grow a team responsible for online feature serving and real-time feature infrastructure

System Architecture: Drive the design and delivery of distributed systems supporting low-latency, high-throughput feature access for inference workloads

Streaming Infrastructure: Shape architecture for streaming pipelines and stateful processing systems that ensure freshness and consistency of ML features

Product Delivery: Own execution of customer-facing features from design through production rollout

Cross-functional Partnership: Partner with Product, Snowpark, ML Platform, and core infrastructure teams to translate enterprise ML requirements into scalable technical solutions

Engineering Excellence: Establish strong engineering practices around performance optimization, observability, reliability, and operational excellence

Technical Contribution: Contribute hands-on to architecture reviews and critical technical decisions

Team Development: Mentor engineers and foster professional growth within the feature store engineering team

What we look for

Technical

Distributed SystemsStrong background in distributed systems fundamentals, including scalability, fault tolerance, consistency, and performance tuning

Real-time SystemsExperience building or operating low-latency, real-time, or online serving systems

Data InfrastructureExperience with large-scale data infrastructure and streaming systems

ML SystemsExposure to machine learning systems, feature engineering workflows, or model serving infrastructure

Cloud ServicesExperience operating highly available, multi-tenant cloud services

Platform ProductsDemonstrated track record of shipping customer-facing platform products at scale

Education

Bachelor's DegreeBachelor's degree in Computer Science, Software Engineering, or related technical field preferred

Advanced DegreeMaster's degree in Computer Science or Engineering preferred but not required

Experience

Technical Experience8+ years of experience building distributed systems, data infrastructure, or backend platform services

Management Experience3+ years of engineering management experience leading high-performing teams

Communication SkillsStrong communication skills and ability to collaborate across engineering and product teams

Skills

Required skills

Engineering Management3+ years leading engineering teams with proven ability to develop talent

Distributed SystemsDeep expertise in building scalable, fault-tolerant distributed architectures

Real-time ProcessingExperience with low-latency serving systems and stream processing frameworks

Performance OptimizationProven track record optimizing system performance and scalability

Cross-functional LeadershipAbility to collaborate across product, engineering, and infrastructure teams

Nice to have

ML InfrastructureExperience with machine learning platforms, feature stores, or model serving

Cloud PlatformsDeep knowledge of AWS, GCP, or Azure cloud services and architectures

Data EngineeringBackground in big data processing, ETL pipelines, and data warehouse systems

Container OrchestrationExperience with Kubernetes, Docker, and cloud-native deployment patterns

ObservabilityExpertise in monitoring, logging, and observability tools for distributed systems

Compensation & benefits

Salary

USD 236,000 – 339,200 (annual)

Benefits

Health Insurance

Comprehensive medical, dental, and vision coverage

Equity Compensation

Stock options and equity participation in company growth

Professional Development

Learning and development opportunities, conference attendance

Work-Life Balance

Flexible work arrangements and generous time off policies

Retirement Benefits

401(k) with company matching contributions

Wellness Programs

Mental health support and wellness initiatives

Interview process

1
Initial Screening — Phone or video screening with recruiter covering background, motivation, and basic role fit
2
Technical Leadership Interview — Discussion of technical experience, system design approaches, and engineering management philosophy
3
System Design Session — Whiteboard or virtual system design exercise focusing on distributed systems and real-time infrastructure
4
Management Interview — Behavioral interview covering leadership experience, team building, and conflict resolution scenarios
5
Cross-functional Panel — Panel interview with stakeholders from Product, Engineering, and ML Platform teams
6
Final Executive Interview — Final conversation with senior leadership covering vision, strategy, and cultural fit