Senior Analyst​/Analyst, Finance Analytics & AI

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.

We are an AI-first analytics team. We don’t use AI to augment traditional BI workflows — we've replaced them. The Finance Analytics team builds the intelligence layer that Strategic Finance runs on: AI agents that encode repeatable finance processes, Streamlit apps that surface real-time insight, semantic models that let any analyst query complex data in plain English, and workflow automations that collapse hours of manual work into a single prompt.

Our primary development environment is Co Co (Cortex Code), Snowflake's AI coding assistant, and Snow Work
, the AI IDE we ship work in. Every deliverable on this team is built AI-first: you design the workflow, you write the prompt, you validate the output. If you are still building dashboards by hand, refreshing Excel files manually, or treating AI as a spell-checker for your code – this role will ask you to operate differently.

This is a high-breadth seat. One week you're building a new AI agent for quarterly revenue analysis; the next you're designing a sensitivity analysis tool for an earnings war room. You are equally comfortable in an AI-IDE, a Python file, and a stakeholder summary for a senior finance leader.

• Design and build skills and agentic experiences that encode repeatable finance workflows – revenue analysis, cost monitoring, earnings prep, headcount tracking – into reusable, invokable tools using CoCo and Snow Work
• Write and iterate on prompt & skill structures (YAML + Markdown skill files) based on output quality and stakeholder feedback
• Build skills that allow non-technical finance analysts to produce analyst-quality output in a single prompt
• Evaluate model outputs rigorously – you are the quality gate before anything reaches a finance stakeholder

• Build and maintain quarterly and weekly revenue summary pipelines
• Support sensitivity analysis models for quarterly business reviews & revenue forecast scenarios
• Produce ad-hoc analysis for Strategic Finance

• Build and improve semantic data models that expose finance tables to natural language queries via Cortex Analyst
• Develop and deploy production finance dashboards as Streamlit apps (locally and deployed to Snowflake)
• Build customer-facing demo applications for Sales and Field teams
• Apply reusable component patterns and shared utility libraries for consistent, polished UI

• Participate in quarterly earnings cycle prep – scenario tooling, export automation, IR data requests
• Build and maintain source-of-truth reporting exports (multi-tab Excel, formatted to spec)
• Support ad-hoc disclosure and investor relations data needs during quarter-end

• AI-assisted development – You have used an LLM coding assistant (CoCo, Cursor, Git Hub Copilot, Claude, or equivalent) as your primary development tool – not an occasional helper, not a code reviewer. You know how to write a prompt that produces production-ready output, how to steer a model that's heading in the wrong direction, and how to encode domain logic into a reusable, parameterized skill.
  
  You have a measurable, trackable record of daily AI usage.
• Prompt engineering and skill authoring – You can write a structured prompt (YAML + Markdown or equivalent) that routes correctly 95% of the time, handles edge cases gracefully, and encodes enough domain knowledge that the model behaves…