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Snowflake is seeking a Principal Software Engineer to lead the design and development of the next-generation Parameters & Experimentation platform. This team builds the configuration, feature-flagging, and experimentation infrastructure that enables Snowflake engineers to introduce capabilities safely, control exposure, and measure outcomes across the AI Data Cloud.
In this role, you will architect and build distributed infrastructure for scalable configuration and feature-flag services with low-latency evaluation, clear consistency guarantees, and predictable failure behavior. You'll make safety scale with change velocity by building automated validation, targeted exposure, progressive rollouts, health-based guardrails, and dependable rollback mechanisms. You'll advance experimentation infrastructure by partnering with engineering and data science teams to build reliable assignment, exposure tracking, and measurement systems that enable evidence-based decision-making.
A key differentiator is the AI-first approach: you'll embed AI-powered capabilities into the platform for configuration discovery, risk assessment, rollout planning, and impact analysis, while establishing rigorous evaluation, security, and reliability standards. You'll lead from architecture through adoption, translating ambiguous needs into practical designs and milestones, delivering intuitive APIs and self-service tools, and guiding incremental migrations across the full lifecycle.
You'll raise the engineering bar by writing and reviewing production code, diagnosing complex failures, owning operational outcomes, and mentoring engineers. You'll influence across teams through technical depth, clear communication, and sound judgment.
Requirements:
- 12+ years of software engineering experience, including technical leadership of complex backend or infrastructure initiatives spanning multiple teams
- Deep experience designing, building, and operating large-scale distributed systems with strong understanding of concurrency, consistency, caching, failure recovery, and performance
- Strong computer science fundamentals and hands-on proficiency in Java, Go, C++, or comparable backend language
- Experience with AWS, Azure, or Google Cloud
- AI-first engineering practice: active use of AI throughout the software lifecycle with demonstrated improvements to execution, problem-solving, and quality; readiness to build AI-powered platform capabilities
- Track record of turning ambiguous problems into delivered systems, balancing architectural investments with incremental progress, and driving adoption across engineering organizations
- Clear written communication, low-ego collaboration, strong ownership, and experience mentoring senior engineers
- Degree in Computer Science or related field, or equivalent practical experience
- Especially relevant: experience with feature-flag or configuration platforms, distributed control planes, progressive delivery, experimentation infrastructure, or AI-powered developer tools
