> Markdown version of [/jobs/ext/4438301-staff-software-engineer-snowhouse](https://www.wearedevelopers.com/jobs/ext/4438301-staff-software-engineer-snowhouse). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Staff Software Engineer - Snowhouse

- **Company:** Snowflake Inc.
- **Location:** Menlo Park, CA, United States
- **Salary:** $339,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Code Review, Databases, Data Infrastructure, Data Warehousing, Distributed Systems, Software Engineering, Data Processing, Information Technology
- **Published:** October 26, 2026
- **Apply:** https://www.adzuna.com/details/5887713020

## About the Role

* 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).

## Description

* 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.