Why This Job is Featured on The SaaS Jobs
Snowflake sits at the infrastructure layer of modern SaaS, where usage data, revenue mechanics, and product adoption signals converge. Featuring a Finance Analytics and AI role here highlights how analytics is shifting from retrospective reporting toward building agentic systems that operationalize decision support across a cloud platform business. The remit spans finance workflows and AI-enabled interfaces, a combination increasingly relevant as SaaS companies standardize on semantic layers and natural-language access to core metrics.
For a SaaS career, the durable value is learning to translate recurring finance motions into reusable tools that stakeholders can invoke on demand. That experience travels well across subscription businesses because it builds fluency in revenue vocabulary, forecasting cycles, and the practical constraints of governance and accuracy. It also develops a rare blend of skills: shipping internal applications, designing data models for self-serve consumption, and treating LLM tooling as part of the development environment rather than an add-on.
This role fits professionals who prefer building systems over producing one-off analyses, and who enjoy being the connective tissue between data, finance, and product-grade tooling. It suits someone comfortable owning deliverables on a weekly cadence, validating model outputs rigorously, and iterating in close partnership with non-technical stakeholders.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
At Snowflake, we are powering the era of the agentic enterprise. 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. 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 Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
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.
This is a high-breadth seat. 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 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 — 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.
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 power natural language analytics
Soft skills required
Translates between AI, data, and finance
Your stakeholders are financial analysts and senior directors who think in Excel models and board decks. 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 are the translation layer between what the model can do and what finance actually needs.
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.
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.
Minimum requirements
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 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)
What success looks like at 90 days
You've built at least two net-new AI agents or workflow tools deployed to the Finance Analytics skill library
You've taken ownership of the quarterly and weekly revenue analysis workflows — they run correctly on schedule without hand-holding
You've shipped at least one Streamlit app to production or a demo application to the Sales Field team
You've participated in at least one quarterly earnings cycle
Your CoCo usage is measurable, consistent, and growing week over week
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.
The analyst this role is backfilling ran over 22,000 AI-assisted development sessions in their first three months. That's the pace expectation.
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
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 benefits information: careers.snowflake.com
