Principal Software Engineer - Parameters & Experimentation

About the role

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.

Build the foundation for faster, safer innovation.

Snowflake’s AI Data Cloud supports critical enterprise workloads across multiple clouds and regions. As AI accelerates software development, the volume and pace of change are outgrowing what engineers can manually review, coordinate, and monitor. Moving faster requires systems that make safe delivery and trustworthy experimentation an integral part of how engineering works.

The Parameters & Experimentation team builds the configuration, feature-flagging, and experimentation infrastructure that enables Snowflake engineers to introduce capabilities, control exposure, and measure outcomes. We’re looking for a hands-on Principal Software Engineer I to help build its next generation, combining deep distributed-systems engineering with an AI-first approach to developer productivity and operational intelligence.

You will shape technical direction, solve challenging systems problems, and lead initiatives across engineering teams. The opportunity is to build the next-generation platform so that increasing development velocity translates into faster customer value without relying on ever-increasing manual oversight.

AS A PRINCIPAL SOFTWARE ENGINEER AT SNOWFLAKE YOU WILL:

- Architect and build distributed infrastructure. Design scalable configuration and feature-flag services with low-latency evaluation, clear consistency guarantees, fault isolation, and predictable behavior during failures.

- Make a safety scale with change velocity. Build automated validation, targeted exposure, progressive rollouts, health-based guardrails, and dependable rollback. Reduce manual coordination while making changes observable, auditable, and recoverable.

- Advance experimentation. Partner with engineering and data science teams to build reliable assignment, exposure tracking, and measurement infrastructure that helps teams evaluate changes and make evidence-based decisions.

- Build and work AI-first. Make AI integral to how you design, implement, test, and operate software. Embed AI-powered capabilities into the platform for configuration discovery, risk assessment, rollout planning, and impact analysis. Establish rigorous evaluation, security, and reliability standards for these capabilities, and drive adoption of AI-first practices across the team.