data
Posted 2 weeks agoSenior/ Staff Analyst, Finance Analytics & AI
at Snowflake
On-site
Requirements
- 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.
- ABOUT THE ROLE 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 CoCo (Cortex Code), Snowflake's AI coding assistant, and SnowWork, 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.
- 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.
- WHAT YOU'LL WORK ON AI AGENT AND WORKFLOW DEVELOPMENT (PRIMARY FOCUS) - 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 CoWork - Write and iterate on prompt & skill structures (YAML + Markdown skill files) based on output quality and stakeholder feedback - Build skills that allows non-technical finance analysts to produce analyst-quality output in a
- 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 like a subject matter expert.
- You think in terms of context, instructions, examples, and output format — not just "the thing I typed before the code came out." Python — Modern, type-hinted, readable.
- You write Python-based applications, data pipelines, and reporting automation.
- You understand caching, session state, and how to structure a multi-page app cleanly.
- SQL — CTEs, window functions, incremental pipeline patterns.
- Data modeling fundamentals — You understand bronze, silver, and gold data models conceptually and contribute to the gold layers and how they translate to semantic layer.
- You know not just how to build a model, but how to version it, evaluate SQL generation accuracy, maintain a verified query library, and iterate based on real analyst feedback.
- A non-technical user should be able to query your model in plain English and get a correct answer.
- STRONG PLUS - Snowflake Cortex — Cortex Analyst, Cortex Agents, AI_SUMMARIZE, AI_EXTRACT, Dynamic Tables, semantic views - SnowWork / CoCo — Prior
- experience deploying agents, authoring skill files, or working within the Snowflake Intelligence ecosystem - Finance literacy — You can read a revenue waterfall, distinguish ARR from NRR, and explain what drives a QoQ change in product revenue - Reporting automation — openpyxl, multi-tab Excel exports formatted to spec, named ranges - dbt — Model authoring, ref() patterns, YAML tests in a cloud warehouse context - Semantic search / embeddings — Vector similarity, embedding-based retrieval, and how they
- experience in analytics, data engineering, or a technical finance adjacent role - Has used an AI coding assistant as a primary development tool — daily usage, not occasional - Proficient in SQL — you can write a window function without looking it up - Has shipped multiple Python applications that end-users actually interacted with; at least one is actively maintained in production - Comfortable working in Git (PRs, branches, code review) - Familiar with fiscal year concepts and core revenue metrics (ARR,
- If you are fluent with AI development tools, you can punch significantly above your level.
- Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth.
Experience
- REQUIREMENTS - 3-5+ years of
Benefits
- How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and
Contact
- benefits information: careers.snowflake.com http://careers.snowflake.com
Additional details
- At Snowflake, we are powering the era of the agentic enterprise.
- 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 the senior level: you've contributed to a shared library or package that others depend on, and you've designed agent orchestration systems — including parallel agent patterns with synthesis layers.
- You don't look up the syntax for a row-numbered deduplication.
- You write prompts and code, but your output needs to make sense to someone who has never opened a terminal.
- You are the translation layer between what the model can do and what finance actually needs.
- You communicate complex ideas simply, ensuring stakeholders understand, trust, and can act on what you build.
- You set the standard for how agents are built on this team.
- Junior analysts look to your skills and code as the reference implementation.
- You push back on shortcuts that create maintenance debt.