# Staff Software Engineer - Snowflake Feature Store **Company:** [Snowflake](https://scaleengineer.com/companies/snowflake) Join Snowflake's ML Feature Store team as a Staff Software Engineer to architect and build cutting-edge feature transformation and serving infrastructure. This role requires 10+ years of experience designing data serving systems and machine learning platforms, with deep expertise in Java, Python, and feature engineering. You'll define the roadmap for Snowflake Feature Store, collaborate across ML teams, and ensure operational excellence while shaping the future of machine learning capabilities in the era of GenAI and agentic enterprises. **Role:** Staff Software Engineer **Seniority:** Staff **Locations:** US-WA-Bellevue **Salary:** 236000–339250 USD [Apply](https://jobs.ashbyhq.com/snowflake/3744bda5-c74a-4fa0-abb2-73da7fcfef09) Canonical: https://scaleengineer.com/jobs/snowflake/staff-software-engineer-snowflake-feature-store --- ## Responsibilities - Define and Own Feature Store Roadmap: Work collaboratively with senior architects and ML team leadership to establish the strategic direction for Snowflake Feature Store, incorporating customer feedback, market trends, and emerging machine learning advances. This includes prioritizing feature development, planning multi-quarter initiatives, and ensuring alignment with broader Snowflake ML suite objectives while advocating for technical debt reduction and infrastructure improvements. - Architect Feature Transformation and Serving Infrastructure: Design and implement robust architectures for complex feature transformations supporting batch and real-time ingestion patterns. Build high-performance, low-latency feature serving systems capable of handling production workloads with strict SLA commitments. Establish scalable patterns for feature lineage tracking, data governance, and model reproducibility across the platform. - Ensure Operational Excellence and Reliability: Drive reliability engineering practices across Feature Store services, defining and monitoring SLOs for availability, latency, and throughput. Lead incident response processes, conduct post-mortem analyses, and implement systematic improvements. Establish observability, monitoring, and alerting frameworks that ensure proactive issue detection and rapid resolution of production incidents. - Execute ML Platform Vision and Innovation: Build and execute the technical vision for incorporating cutting-edge advances in machine learning, including support for emerging GenAI and agentic enterprise patterns. Evaluate emerging technologies and research innovations that can enhance feature management capabilities, ensuring Snowflake remains at the forefront of ML infrastructure evolution. - Cross-Team Collaboration and Integration: Collaborate across ML partner teams including model training, inference serving, and analytics to improve development velocity and platform capabilities. Design clean APIs and integration points that enable seamless workflows between Feature Store and other Snowflake ML products. Facilitate knowledge sharing and establish best practices across the broader ML engineering organization. - Technical Team Leadership and Mentorship: Support and elevate team members in delivering high technical quality through code review, architectural guidance, and professional development. Foster a culture of technical excellence, system design thinking, and continuous learning. Conduct design reviews, establish coding standards, and mentor junior and mid-level engineers on building production-grade distributed systems. ## Requirements ### education - {"name":"Bachelor's Degree in Computer Science","description":"B.Sc. in Computer Science or closely related field required. Should provide strong foundation in computer science fundamentals including algorithms, data structures, complexity analysis, and system design principles essential for architecting large-scale distributed systems."} ### technical - {"name":"Java and Python Fluency","description":"Production-level fluency in both Java and Python. Java expertise should cover enterprise-scale system development with deep understanding of concurrency, performance optimization, and JVM internals. Python expertise should span data science libraries, ML frameworks, and systems-level programming."} - {"name":"Distributed Systems Design","description":"Deep understanding of distributed systems concepts including consensus algorithms, data replication strategies, eventual consistency models, and failure handling. Should demonstrate ability to design systems handling high throughput, low latency, and fault tolerance requirements."} - {"name":"Feature Engineering Platform Knowledge","description":"Strong technical knowledge of feature engineering platforms, including feature definition and management, feature transformation logic, real-time vs. batch serving trade-offs, and feature lineage tracking. Familiarity with the operational complexities of managing features across the ML lifecycle."} - {"name":"Data Systems Performance","description":"Expertise in optimizing data systems for performance, including profiling, caching strategies, query optimization, and understanding of I/O bottlenecks. Should include practical experience with column-oriented storage, indexing strategies, and cost-performance optimization in cloud environments."} ### experience - {"name":"10+ Years Data Infrastructure and ML Platform Development","description":"Minimum of 10 years of professional experience designing, building, and operating large-scale data serving infrastructure and machine learning platforms in production environments. This should include substantial experience with distributed systems architecture, handling petabyte-scale data, and supporting mission-critical workloads."} - {"name":"ML Systems Production Experience","description":"Demonstrated track record working with machine learning systems and platforms in production, including deep familiarity with feature engineering workflows, model training pipelines, inference serving, and the operational challenges of maintaining ML infrastructure at scale."} - {"name":"Leadership in Technical Architecture","description":"Proven experience in defining technical direction, leading architectural decisions on complex systems, and collaborating with senior leadership to establish product roadmaps. Should include mentoring other engineers and raising team technical capabilities."} ## Skills ### required - {"name":"Java","description":"Production-level proficiency in Java for building scalable, high-performance data systems and service architectures"} - {"name":"Python","description":"Advanced fluency in Python for machine learning frameworks, data transformation pipelines, and ML platform development"} - {"name":"Feature Engineering Platform Design","description":"Deep experience architecting and building feature engineering platforms that support complex transformations and feature lineage tracking"} - {"name":"Machine Learning Systems Architecture","description":"Comprehensive understanding of ML platform architecture, model serving infrastructure, and feature store design patterns"} - {"name":"Data Infrastructure Design","description":"Proven expertise designing and implementing high-performance data serving infrastructure, including caching strategies and low-latency optimization"} - {"name":"Computer Science Fundamentals","description":"Strong foundation in algorithms, data structures, distributed systems, and system design principles applicable to large-scale platforms"} - {"name":"Technical Leadership","description":"Demonstrated ability to define product roadmaps, mentor engineers, and drive technical decision-making across teams"} ### preferred - {"name":"Distributed Systems Experience","description":"Background with large-scale distributed database systems, real-time data streaming, or cloud infrastructure platforms"} - {"name":"Feature Store or ML Platform Experience","description":"Previous experience with commercial or open-source feature stores such as Tecton, Feast, or similar ML infrastructure platforms"} - {"name":"Data Warehouse Technologies","description":"Familiarity with Snowflake, BigQuery, Redshift, or other modern cloud data platforms and their integration with ML workflows"} - {"name":"Real-Time Serving Systems","description":"Experience building low-latency feature serving systems handling high-throughput production traffic patterns"} - {"name":"GenAI and LLM Applications","description":"Understanding of how AI agents and large language models consume features and require novel data architecture approaches"} - {"name":"Open Source Contributions","description":"Active participation in open-source data infrastructure, ML platform, or feature engineering projects"} ## Tech stack ### tools - {"name":"Git and GitHub","description":"Version control and collaboration platform for managing codebase and infrastructure-as-code"} - {"name":"Docker and Kubernetes","description":"Containerization and orchestration technologies for deploying and scaling feature serving infrastructure"} - {"name":"CI/CD Pipelines","description":"Automated testing, building, and deployment systems for reliable and rapid feature releases"} - {"name":"Monitoring and Observability Tools","description":"Prometheus, Grafana, DataDog, or similar platforms for system monitoring, alerting, and performance analysis"} - {"name":"AWS/GCP/Azure Cloud Platforms","description":"Cloud infrastructure and managed services for deploying distributed systems and data pipelines"} ### others - {"name":"MLOps Practices","description":"Comprehensive understanding of ML operationalization including feature governance, model reproducibility, and production monitoring"} - {"name":"Data Governance and Compliance","description":"Knowledge of data lineage tracking, privacy regulations (GDPR, CCPA), and data governance frameworks for enterprise ML systems"} - {"name":"Performance Profiling and Optimization","description":"Expertise in identifying and resolving performance bottlenecks using profiling tools and algorithmic optimization techniques"} - {"name":"Agile and Collaborative Development","description":"Experience working in fast-paced, collaborative team environments with emphasis on rapid iteration and experimental mindset"} ### databases - {"name":"Snowflake Data Warehouse","description":"Cloud-native data warehouse platform serving as the primary data repository for feature storage and transformation"} - {"name":"Redis or Memcached","description":"In-memory caching layers for low-latency feature serving in production environments"} - {"name":"PostgreSQL and MySQL","description":"Operational databases used for metadata management, feature definitions, and system state in ML platforms"} - {"name":"Data Streaming Systems","description":"Kafka, Pulsar, or similar platforms for real-time feature data ingestion and processing pipelines"} ### languages - {"name":"Java","description":"Primary language for building scalable backend services and distributed systems at Snowflake"} - {"name":"Python","description":"Essential for ML platform development, data transformations, and ML model training pipeline integration"} - {"name":"SQL","description":"Critical for feature definition, data transformation logic, and querying data warehouse systems"} - {"name":"Scala","description":"Commonly used in distributed data processing frameworks and ML infrastructure development"} ### frameworks - {"name":"Apache Spark","description":"Distributed computing framework for large-scale feature transformation and data processing pipelines"} - {"name":"TensorFlow and PyTorch","description":"Deep learning frameworks commonly integrated with feature stores for model training and serving workflows"} - {"name":"Pandas and NumPy","description":"Fundamental Python libraries for data manipulation, feature engineering, and statistical analysis"} - {"name":"gRPC","description":"High-performance RPC framework for real-time feature serving at low latency in production systems"} ## Benefits ### benefits - {"name":"Competitive Health and Wellness Benefits","description":"Comprehensive health, dental, and vision coverage with flexible spending accounts. Wellness programs including fitness subsidies, mental health support, and preventive care initiatives."} - {"name":"Equity and Stock Options","description":"Substantial equity grants providing ownership stake in Snowflake's growth and long-term value creation. Stock options vesting over standard four-year schedules with employee stock purchase plans."} - {"name":"Retirement Planning","description":"401(k) retirement plans with competitive company matching contributions and investment flexibility. Financial planning resources and retirement counseling services."} - {"name":"Professional Development and Learning","description":"Annual professional development budgets for conferences, training programs, and certifications. Internal learning platforms, technical mentorship programs, and access to industry-leading educational resources."} - {"name":"Generous Paid Time Off","description":"Unlimited vacation policy emphasizing work-life balance and recovery. Paid parental leave, sabbatical opportunities, and flexibility for personal development and family time."} - {"name":"Flexible Work Arrangements","description":"Flexible work-from-home options with collaborative office environments. Remote work stipends for home office setup and flexible scheduling to support individual productivity preferences."} - {"name":"Life and Financial Protection","description":"Comprehensive life insurance, disability coverage, and financial protection programs. Employee assistance programs providing confidential counseling and support services."} - {"name":"Technology and Equipment","description":"Latest development workstations, laptops, and technical equipment. Software licenses and tools subscriptions necessary for effective engineering work and system design."} ## Compensation - **max:** 300000 - **min:** 220000 - **currency:** USD - **stockOptions:** true ## Interview process ### steps - {"name":"Initial Recruiter Screening","description":"30-minute conversation with Snowflake recruiting team to discuss background, experience with feature stores or ML platforms, career motivations, and general fit with the Staff Engineer role. Discuss your experience architecting large-scale systems and collaborating with cross-functional teams."} - {"name":"Technical Architecture Interview","description":"90-minute in-depth technical discussion covering distributed systems design, feature store architecture, and machine learning platform considerations. You'll discuss past system designs, trade-off decisions, scalability challenges, and how you'd approach building low-latency feature serving infrastructure. May include whiteboarding sessions on system design problems."} - {"name":"ML Platform and Domain Knowledge Interview","description":"60-minute conversation with ML platform engineers or data infrastructure specialists covering deep technical knowledge of ML systems, feature engineering practices, real-time serving patterns, and Snowflake's ML suite vision. Discussion of how you've tackled operational challenges in production ML systems and your philosophy on platform design."} - {"name":"Leadership and Collaboration Panel","description":"60-minute interview with senior engineers and engineering leaders assessing your vision for the Feature Store roadmap, technical leadership style, mentorship approach, and ability to collaborate across teams. Discussion of how you've influenced technical strategy, driven organizational improvements, and elevated team capabilities in previous roles."} - {"name":"Hiring Manager Deep Dive","description":"45-minute final interview with the Staff Engineer role's hiring manager covering role expectations, growth opportunities within Snowflake's ML organization, your long-term technical interests, and mutual fit evaluation. Discussion of how you approach experimental thinking, learning from emerging AI and ML advances, and contributing to Snowflake's agentic enterprise vision."} - {"name":"Executive or Peer Collaboration Session","description":"Optional 30-45 minute conversation with Snowflake ML leadership or peer staff engineers to discuss broader strategic direction, insights on ML infrastructure industry trends, and your perspective on Feature Store's role in Snowflake's product ecosystem."} ## Full description At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Where Data Does More. Join the Snowflake team. Join our ML Feature Store team where we're building cutting-edge product capabilities that power complex feature transformations and low latency feature serving. We're revolutionizing machine learning feature management and serving capabilities as part of the Snowflake ML suite of products. In the era of GenAI and agents, our team delivers high-quality, fresh feature solutions that make a real difference for our customers. **IN THIS ROLE AT SNOWFLAKE, YOU WILL:** * Help define and own the roadmap for Snowflake Feature Store, working collaboratively with senior architects and ML team leadership * Build and execute a vision for incorporating new advances in machine learning * Ensure operational excellence of services and meet reliability, availability, and performance commitments * Collaborate across ML partner teams to improve development velocity and capabilities * Support team members in delivering high technical quality **WE WOULD LOVE TO HEAR FROM YOU IF YOU HAVE:** * 10+ years of experience in designing and building data serving infrastructure and/or machine learning platforms. * Strong track record working with machine learning systems and platforms. * Strong understanding of computer science fundamentals. * [B.Sc](http://B.Sc). in Computer Science * Fluency in Java and Python * Experience with feature engineering platforms and ML platforms Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: [careers.snowflake.com](http://careers.snowflake.com)