Location: This is a hybrid role in Menlo Park, CA

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 CoWork, 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 deal benchmarking agent that surfaces peer comparison data for a deal desk manager seconds before a negotiation; the next you're designing a margin calculator that lets a sales rep model deal economics live on a call. You are equally comfortable in an AI-IDE, a Python file, and a stakeholder summary for a deal desk director.
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 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 deal desk operations - discount trend analysis, concession benchmarking, pipeline deep dives, and capacity utilization summaries for renewal planning

Deal desk intelligence

• Build and maintain the deal benchmarking and margin analysis tools used by deal desk managers in live negotiations - accuracy directly impacts pricing decisions
• Develop consumption and overage analytics that surface which accounts are trending toward underage (rollover risk) or overage (expansion opportunity) ahead of their renewal
• Automate the quarterly deal desk reporting pack - closed deal summaries, concession trends, rip-and-replace analysis, early renewal cadence, and edition splits by service level
• Build and iterate on AI skills (SKILL.md prompt files) that encode deal desk workflows: peer benchmark lookup, ACV suggestion, effective discount recommendation, and approval queue management
• Partner with deal desk managers to translate deal structure logic and pricing conventions into data models and AI agents that surface the right recommendation at the right moment

Semantic Layer & Application development

• Own semantic layers end-to-end - model design, versioning strategy, verified query coverage, and accuracy iteration based on eval metrics; not just build models, but maintain the contract between the model and its consumers across each quarterly iteration
• 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. 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. 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.

SQL - CTEs, window functions, incremental pipeline patterns. You don't look up the syntax for a row-numbered deduplication.

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
• 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

Soft skills required

Translates between AI, data, and finance

Your stakeholders are deal desk managers and finance directors who think in discount approval thresholds, renewal ACV targets, and pipeline call accuracy. You write prompts and code, but a deal desk manager needs to trust that the benchmarks you surface are accurate enough to use in a live negotiation. You are the translation layer between what the model can do and what deal desk 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. You don't wait to be asked to improve shared infrastructure.
Thinks in workflows, not tasks

You don't just answer a question - you build a tool that answers it forever. When asked to do something twice, you automate it. Your instinct is to encode work into a reusable agent, not to redo it manually each week. At the senior level, this extends to the team: when the team does something repeatedly, you build the shared infrastructure that makes everyone faster.
Works fast with high accuracy

The role runs on a weekly cadence tied to finance deliverables. You scope, build, and ship a working artifact in 1-2 days. Accuracy matters more than speed - but accuracy is not a reason to be perpetually slow.
Comfortable with ambiguity

The brief is often: "Can you build something like the earnings tool, but for sensitivity analysis?" You scope it, build a working prototype, and come back for feedback - not a list of clarifying questions.

Minimum requirements

• 2-4+ 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 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)

What success looks like at 90 days

• You've taken ownership of a deal desk domain - consumption analytics, discount benchmarking, or pipeline intelligence
• You've shipped at least one Streamlit app to production or a demo application to the Finance Workloads team
• You've participated in at least one quarterly earnings cycle or deal desk quarterly reporting pack
• You've contributed a module, skill, or shared component to the team's shared infrastructure - something other analysts use without you having to explain it

Why this role is unusual at this level

This seat asks you to do all of that and build the AI infrastructure that makes the entire Finance Analytics team faster. You are simultaneously a practitioner and a workflow engineer.

If you are fluent with AI development tools, you can punch significantly above your level. At the senior level, you are not just building the infrastructure - you are deciding what it should be. That means making architectural calls that hold across quarters, not just shipping the next feature.