Staff Software Engineer - Snowhouse
Staff Engineer · Staff · Full Time
Opens Snowflake's application page
Role
What you'll do.
Join Snowflake's Snowhouse Foundation team as a Staff Software Engineer to architect and lead the development of globally distributed data warehouse infrastructure processing petabyte-scale datasets. This role combines hands-on technical execution with strategic leadership, requiring 12+ years of distributed systems expertise and deep cloud infrastructure knowledge to drive critical investments in data processing, high-performance ingestion/export, and system database optimization while mentoring engineers and establishing engineering excellence standards.
Responsibilities
- Technical Leadership and Mentorship: Provide hands-on code reviews and guidance to engineering teams, establish and maintain best practices across the Snowhouse Foundation team, mentor mid and senior-level engineers to elevate technical standards, and contribute directly to team strategy and long-term platform roadmap while fostering a culture of technical excellence.
- Distributed Systems Architecture Design: Design and implement highly available, fault-tolerant distributed platforms and pipelines capable of handling petabyte-scale data processing globally. Architect resilient components for data warehouse infrastructure including replication layers, ingestion systems, and cross-environment data synchronization across multiple cloud providers and on-premise deployments.
- Data Infrastructure Development: Develop and optimize core data processing infrastructure components including high-performance data export and ingestion systems, optimized data layout strategies, and compliance-driven architectural decisions. Drive technical execution on complex system design challenges with focus on scalability, performance, and reliability at massive scale.
- Cross-Functional Project Leadership: Lead end-to-end engineering initiatives spanning multiple quarters and teams, from initial problem definition through production deployment. Develop deep understanding of underlying business and user requirements by collaborating with product managers, data science teams, and business units to deliver comprehensive data platform capabilities.
- Platform Reliability and Observability Ownership: Own or co-own reliability, observability infrastructure, SLOs, capacity planning, and durable remediation strategies for assigned problem spaces within the Snowhouse Foundation. Establish monitoring and alerting frameworks, drive incident response improvements, and ensure service resilience across global deployments.
- Cross-Team Collaboration on Infrastructure Concerns: Partner across engineering teams to drive improvements in shared concerns such as system reliability, operational efficiency, and performance optimization. Influence platform-wide decisions regarding data processing infrastructure, resource consumption patterns, and customer visibility into global account activities and usage metrics.
Qualifications
What we look for.
Technical
Distributed Systems Architecture
Advanced expertise in designing, implementing, and operating large-scale distributed systems with deep understanding of consistency models, consensus algorithms, replication strategies, and fault tolerance patterns. Proven ability to design systems handling massive concurrent operations and data volumes.
Data Warehouse and Infrastructure Engineering
Demonstrated experience building or significantly contributing to data warehouse systems or data infrastructure platforms. Strong understanding of data processing pipelines, query optimization, storage systems, and distributed query execution across heterogeneous environments.
Cloud Platform Engineering
Deep hands-on experience developing resilient, production-grade services across major public cloud platforms (AWS, Azure, or GCP). Proficiency with cloud-native services, container orchestration, infrastructure-as-code, multi-region deployments, and disaster recovery patterns.
Database Systems Knowledge
Strong foundation in database fundamentals including transaction management, indexing strategies, query planning, storage engines, and performance optimization. Understanding of both relational and distributed database architectures relevant to modern data warehouse design.
System Design and Problem-Solving
Track record of solving complex system design challenges involving tradeoffs between consistency, availability, partition tolerance, scalability, and maintainability. Ability to make architectural decisions that balance immediate requirements with long-term technical sustainability.
AI-Assisted Engineering Practices
Proficiency leveraging modern AI development tools and assistants to improve code quality, accelerate development velocity, and maintain rigorous engineering standards. Demonstrated ability to critically evaluate AI-generated suggestions and maintain human oversight in technical decision-making.
Education
Bachelor's Degree in Computer Science or Related Field
BS in Computer Science, Computer Engineering, or equivalent technical discipline providing strong foundational knowledge in algorithms, data structures, and computer architecture.
Advanced Degree (Preferred)
MS or PhD in Computer Science, Engineering, or related field demonstrating advanced expertise in distributed systems, databases, or systems optimization. However, equivalent practical experience is equally valued.
Equivalent Practical Experience
Exceptional candidates with 12+ years of demonstrated hands-on experience building and operating large-scale distributed systems may substitute formal education requirements, provided they demonstrate mastery of core CS fundamentals through their work.
Experience
12+ Years Software Development in Distributed Systems
Minimum 12 years of professional software engineering experience with substantial focus on distributed systems development, architecture, and operation. Career progression demonstrating increasing complexity of owned systems and scope of technical influence.
Data Warehouse or Infrastructure Leadership
Significant experience (5+ years) leading, architecting, or making critical technical contributions to data warehouse systems, data processing infrastructure, or analytics platforms serving production workloads at enterprise or hyperscaler scale.
Large-Scale Systems Operation Experience
Demonstrated experience operating, troubleshooting, and optimizing systems handling petabyte-scale data volumes or millions of concurrent requests. Proven ability to navigate production incidents, implement observability improvements, and drive operational excellence.
Cross-Functional Technical Leadership
Experience leading technical projects and initiatives across multiple teams, establishing engineering standards, conducting impactful code reviews, and mentoring senior engineers. History of influencing technical strategy and architectural decisions at organizational level.
Skills
Required
Distributed Systems Design
Core expertise in CAP theorem, eventual consistency, replication protocols, load balancing, and resilience patterns for systems processing data at planetary scale.
Java or C++
Production-level proficiency in at least one systems programming language commonly used for building high-performance data infrastructure and distributed systems components.
SQL and Query Optimization
Strong SQL expertise including query planning, optimization techniques, statistics gathering, and understanding of how query engines translate SQL to distributed execution plans.
Cloud Infrastructure and DevOps
Hands-on experience with cloud platforms (AWS, Azure, GCP), containerization technologies, infrastructure automation, and deployment pipeline management for production systems.
Technical Communication
Excellent ability to articulate complex technical concepts to diverse audiences including engineers, product managers, and executives. Strong documentation and design review communication skills.
Collaborative Problem-Solving
Demonstrated ability to work effectively across teams, synthesize diverse perspectives, facilitate technical discussions, and drive consensus on complex architectural decisions.
Preferred
Data Orchestration and ML Pipeline Experience
Nice to haveFamiliarity with data orchestration systems (Apache Airflow, Prefect, Dagster) or ML orchestration platforms across diverse architectures. Experience building systems that schedule and monitor complex data processing workflows.
Petabyte-Scale System Experience
Nice to haveHands-on experience operating and optimizing systems handling petabyte-scale datasets, massive parallel processing, or similar high-volume data environments at hyperscaler companies.
Snowflake or Competitive Data Warehouse Knowledge
Nice to haveDirect experience using, deploying, or operating Snowflake or competitive cloud data warehouse platforms (Redshift, BigQuery, Azure Synapse). Understanding of their architectural patterns and capabilities.
Incident Response and Site Reliability
Nice to haveExperience leading incident response for critical systems, implementing SLOs and error budgets, driving postmortem processes, and establishing observability and monitoring best practices.
Network and Storage Optimization
Nice to haveKnowledge of network protocols, distributed storage systems, data replication strategies, and optimization techniques for efficient data transfer across geographic regions and network boundaries.
Contributing to Open Source Infrastructure Projects
Nice to haveActive contributions to open-source distributed systems, data infrastructure, or database projects demonstrating advanced technical expertise and community involvement.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 236,000 – 339,200
Equity·Stock options
Benefits
Comprehensive Health Coverage
Medical, dental, and vision insurance plans with company contributions to premiums. Coverage extends to employees and eligible family members with multiple plan options.
Retirement and Financial Planning
401(k) retirement savings plan with company matching contributions, financial planning services, and investment advisory resources to support long-term wealth building.
Generous Time Off Policy
Unlimited paid time off (PTO) for most roles, flexible work arrangements, and paid parental leave to support work-life balance and family needs.
Professional Development and Learning
Tuition reimbursement, training budgets, conference attendance support, and access to online learning platforms to support continuous skill development and career growth.
Equity Compensation
Significant equity grants for staff-level positions aligned with company growth, providing opportunities to participate in long-term value creation alongside competitive salary compensation.
Wellness and Mental Health Benefits
Access to wellness programs, gym memberships, mental health counseling, stress management resources, and meditation apps supporting holistic employee wellbeing.
Remote Work Flexibility
Flexible work arrangements including remote work options and hybrid schedules enabling employees to balance office collaboration with distributed work productivity.
Commuter and Transportation Benefits
Pre-tax transportation benefits, parking assistance, and commuter program support for employees commuting to Snowflake offices and headquarters locations.
Full posting
Original listing.
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.
Staff Software Engineer - Snowhouse Foundation
About the Team
The Snowhouse Foundation team builds our globally distributed data warehouse, managing vast petabyte-scale datasets that are continuously ingested, processed, and replicated across all Snowflake environments and external data sources. Snowhouse powers Snowflake's core business, engineering, and data science operations while delivering critical customer visibility into global account activities, usage, and resource consumption. The team drives critical investments in core data processing infrastructure, high-performance data export/ingestion, optimized data layout and compliance, and Snowflake's system database and applications.
A successful candidate will:
Deeply understand the inner workings of Snowflake and the needs of our users, exhibiting a healthy curiosity for use cases and needs that will inform future technical strategy, anticipating needs rather than reacting to them.
Be highly productive by leveraging AI-assisted engineering, empowering and creating opportunities for others, and effectively leading at scale.
Show a natural inclination to partner across teams to deliver improvements on cross-team concerns such as reliability and efficiency.
Responsibilities
Technical Leadership: Provide hands-on guidance and code reviews, develop other engineers and raise the quality bar, establish engineering best practices across the team and contribute to the team strategy and future of the platform.
Technical Execution: Design and implement highly available distributed platforms, pipelines, and data infrastructure components to scale global data processing. Solve hard problems, use AI-assisted engineering responsibly to improve velocity and quality.
Problem-space Ownership: Lead cross-functional engineering projects from idea inception through implementation and production deployment across multiple quarters and teams. Develop a deep understanding of the underlying user and company needs.
Cross-Functional Collaboration: Partner closely with product managers, senior ICs, data science teams, and business units to deliver end-to-end data platform capabilities.
Platform Ownership: Own or co-own the reliability, observability, SLOs, capacity, and durable remediation goals for their problem space.
Qualifications
Experience: 12+ years of software development experience in distributed systems, with a strong focus on data warehouse or data infrastructure engineering.
Cloud Infrastructure: Deep experience developing resilient, large-scale services in public cloud environments (AWS, Azure, or GCP).
Technical Depth: Demonstrated proficiency in distributed systems architecture and database fundamentals, with a track record of solving complex system design challenges.
Communication: Excellent technical communication and collaborative problem-solving skills across multidisciplinary teams.
Bonus Skills: Familiarity with data or ML orchestration systems across diverse architectures.
Education: BS, MS, or PhD in Computer Science or a related technical field (or equivalent practical experience).
Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee’s duty to keep customer information secure and confidential.
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
Redirects to Snowflake's application page.
Other roles
More at Snowflake.
Software Engineer, Full Stack - Marketplace
Mid
Senior Manager - Engineering Systems AI Developer Experience
Manager
Engineering Manager - Cost Intelligence
Manager
Staff Software Engineer, Snowpark Container Service
Staff
Software Engineer AI Team
Mid