Analyst, Finance Analytics & AI

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 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

Finance analytics

• 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

Semantic Layer & Application development

• 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

Earnings and reporting automation

• 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

Hard skills required

Must-have

• AI-assisted development — You have used an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent) as your primary development tool
• Prompt engineering and skill authoring — You can write a structured prompt (YAML + Markdown or equivalent)
• Python — Modern, type-hinted, readable. You write Python-based applications, data pipelines, and reporting automation
• SQL — CTEs, window functions, incremental pipeline patterns
• Data modeling fundamentals — You understand semantic layers

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
• Finance literacy — You can read a revenue waterfall, distinguish ARR from NRR
• Reporting automation — openpyxl, multi-tab Excel exports
• dbt — Model authoring, ref() patterns, YAML tests
• Semantic search / embeddings — Vector similarity, embedding-based retrieval

Minimum requirements

• 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
• Proficient in SQL
• 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)