Why This Job is Featured on The SaaS Jobs
This Finance Analytics and AI Deal Desk seat stands out in the SaaS ecosystem because it sits directly on the commercial engine of a subscription business, where pricing, discounting, renewals, and consumption signals shape outcomes. The remit also reflects a broader shift in modern SaaS operators: embedding AI into internal decision systems, not as a side project, but as the default interface for finance and go to market teams.
For a SaaS career, the durable value is the combination of technical analytics craft with revenue-adjacent context. Building semantic layers, lightweight applications, and automated workflows tied to forecasting and deal execution develops a systems view of how product usage, contract structure, and margin interact. Experience translating stakeholder needs into reusable tools is highly portable across SaaS finance analytics, revenue operations, and data product roles.
The role is best suited to professionals who prefer ownership over isolated analysis and who like shipping artifacts that others rely on, such as apps, agents, and shared components. It will fit someone comfortable moving between SQL and Python work and stakeholder-facing reasoning, with an interest in applying AI-assisted development to operational analytics where correctness and trust matter.
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.
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.
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
