Staff Software Development - Full Stack

About this 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.

Location: Hybrid (3 days) in our Dublin office.

We're seeking a Staff Software Engineer – Full Stack in Dublin, California to build and scale the applications and AI agents that power Snowflake's revenue accounting and quote-to-cash processes.

You bring 8+ years of full-stack development experience, with deep expertise in AI/ML-powered and agentic systems, including the harnesses that make agents reliable. You've partnered with Revenue Accounting teams, integrated with ERP platforms such as SAP or Oracle, and built AI agents that automate multi-step business workflows. You design scalable applications, integrate LLM capabilities, and raise engineering productivity with tools like Claude Code and Snowflake Cortex

AS A STAFF SOFTWARE DEVELOPMENT - FULL STACK FOR THE ET APPLICATIONS TEAM, YOU WILL:

• Lead the design and delivery of full-stack applications and agentic systems for Snowflake's finance organization

• Architect, build, and deploy AI agents that plan, call tools, and act across finance workflows such as contract review, reconciliation triage, exception resolution, and close-task automation

• Own the agent harness (agent loop, tool definitions, context and memory management, permissions, and sandboxed execution) so agents behave predictably on real financial data