Analyst, Finance Analytics & AI at Snowflake in Menlo Park, CA

Summary: Snowflake is a company that is redefining the future of work through AI-driven solutions. The role involves developing AI agents and workflows for finance analytics, automating reporting processes, and ensuring high-quality outputs for stakeholders. Responsibilities: - 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 SnowWork - 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 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 Required Qualifications: - 1–3 years of 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 at least one Python application that end-users actually interacted with - Comfortable working in Git (PRs, branches, code review) - Familiar with fiscal year concepts and core revenue metrics (ARR, bookings, NRR) - AI-assisted development — You have used an LLM coding assistant (CoCo, Cursor, GitHub 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 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. You don't look up the syntax for a row-numbered deduplication - Data modeling fundamentals — You understand semantic layers, and how to build a model that a non-technical user can query in plain English Preferred Qualifications: - 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 power natural language analytics Required Skills: AI-assisted development, Prompt engineering, Skill authoring, Python, SQL, Data modeling, Snowflake Cortex, SnowWork, Finance literacy, Reporting automation, dbt, Semantic search, Translation between AI, data, finance, Workflow thinking, Accuracy Benefits: Medical, dental, vision, life, and disability insurance, 401(k) retirement plan, Flexible spending & health savings account, At least 12 paid holidays, Paid time off, Parental leave, Employee assistance program, Other company benefits