AI Analytics Engineer (Finance)

TL;DR

AI Analytics Engineer (Finance): Building AI agents and intelligence layers for strategic finance workflows with an accent on agentic experiences and prompt engineering. Focus on automating revenue analysis, deal benchmarking, and deploying production Streamlit apps to replace traditional BI workflows.

Location: Hybrid in Menlo Park, CA

Salary: $114K – $150.1K

Company

Snowflake is an AI-first data platform powering the era of the agentic enterprise through advanced analytics and AI integration.

What you will do

• Design and build agentic experiences and reusable tools for finance workflows including revenue analysis, cost monitoring, and headcount tracking.
• Develop and iterate on structured prompt and skill files (YAML/Markdown) to ensure high-quality model outputs for non-technical analysts.
• Maintain quarterly and weekly revenue summary pipelines and support sensitivity analysis models for business reviews.
• Build deal benchmarking and margin analysis tools used by deal desk managers during live negotiations.
• Own end-to-end semantic layers, managing model design, versioning, and accuracy iteration.
• Deploy production finance dashboards and customer-facing demo applications using Streamlit.

Requirements

• 2-4+ years of experience in analytics, data engineering, or a technical finance role.
• Proven track record of using LLM coding assistants (e.g., CoCo, Cursor, Copilot) as a primary daily development tool.
• Proficiency in SQL (CTEs, window functions) and modern, type-hinted Python.
• Experience shipping and maintaining Python applications in production.
• Comfortable working with Git, including PRs and code reviews.
• Strong understanding of data modeling fundamentals (bronze, silver, gold layers).

Nice to have

• Experience with Snowflake Cortex (Analyst, Agents) and the Snowflake Intelligence ecosystem.
• Knowledge of dbt model authoring and ref() patterns.
• Experience with semantic search, embeddings, and vector similarity.
• Prior ownership of deal desk domains or participation in quarterly earnings cycles.
• Experience deploying Streamlit apps to production.

Culture & Benefits

• AI-first culture where AI is treated as a high-trust collaborator to accelerate impact.
• Fast-paced, experimental environment focused on redefining how work gets done.
• High-breadth role with significant influence over AI infrastructure and workflow engineering.
• Opportunity to set technical standards and mentor junior analysts.