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.

ABOUT THE TEAM

Strategic Finance is the commercial and analytical engine behind Snowflake's product and revenue decisions. The team operates at

the intersection of product strategy, pricing, and investment prioritization. Product Finance, the function this role sits within,

partners with each product organization on planning, performance, and pricing. We work closely with Product and Engineering

leadership, and we partner across Sales, Business Development, and the Executive Leadership Team. Every consequential investment,

monetization, and pricing decision at Snowflake runs through analysis this team produces.

ABOUT THE ROLE

We are hiring a senior strategic finance analyst to support Snowflake's product portfolio. You will be the analytical engine

behind the team's recommendations to Product, Engineering, Marketing, and Sales leadership. You will build the models, run the

analyses, and produce the narratives that shape decisions on what we build, how we launch, how we monetize, and where we invest.

You will translate product usage, customer behavior, unit economics, and market signals into clear data-driven recommendations.

You will work directly with leaders across the product organizations you support and develop into a full strategic finance partner

as the team scales. This role can be deployed across Snowflake's product workload pillars (AI and Analytics, Data Engineering and

Data Lake, Platform). Specific vertical coverage is determined at hiring.

IN THIS ROLE YOU WILL

Build and Operate Financial Models. Build and maintain the driver-based revenue models, workload P&Ls, unit economics, and

pricing models that anchor planning and pricing decisions for your product workloads. Refresh on a recurring cadence; explain

deltas with rigor.

Run the Forecast and Performance Cycle. Run the monthly and quarterly forecast cycle, plan vs. actual attribution with

driver-based variance breakdowns, and the Quarterly Business Review data pack. Variance commentary ties to a defined set of

drivers plus a clean initiative narrative.

Translate Data Into Insights. Translate product usage, customer behavior, cohort and funnel dynamics, and financial data into

clear insights and recommendations for the Lead and Product partners. Surface what is gaining or losing traction and why.

Pricing and Monetization Analysis. Build the unit economics, willingness-to-pay benchmarks, and competitive pricing analyses

that support pricing and packaging decisions. Produce Pricing Committee inputs.

Investment Business Cases. Build the financial models and analytical scaffolding for major product and go-to-market investment

business cases. Return on investment, investment sizing, sensitivity analysis, comparative tradeoffs.

Build Reusable Tools and Reporting. Build the dashboards, automated reports, and reusable model templates that make recurring

analytical work faster and more reliable for the team. Default to automation over manual recurring work.

Executive-Ready Narratives. Produce decision-ready narratives, charts, and slides that survive senior leadership review on

first submission. Communicate complex quantitative analysis in clear executive language.

Stay Current. Stay current on cloud, software-as-a-service, product-led growth, consumption-based pricing, and competitive

dynamics relevant to the workloads you cover. Bring market context into recurring analyses.

WHAT YOU WILL NEED

REQUIRED

4+ years of experience in strategic finance, product finance, growth equity or private equity, investment banking, management

consulting, or equivalent analytical roles, with at least some operating experience at a fast-paced technology company.

Exceptional financial modeling skills. Demonstrated ability to build product profit and loss views, cohort and funnel analyses,

pricing models, unit economics, and operating models that connect strategy to growth decisions.

SQL fluency. Comfortable querying large product analytics datasets in a cloud data platform. Snowflake or similar.

Strong analytical judgment and attention to detail. Ability to evaluate opportunities, tradeoffs, and risks in a fast-moving

product environment.

Working understanding of cloud and software-as-a-service product economics. Curiosity about how product workloads are priced,

packaged, and scaled.

The ability to translate complex product, usage, customer, and financial data into clear recommendations for senior leadership.

High attention to detail and a commitment to accuracy, paired with comfort making decisions when data is imperfect and the

business is evolving.

Effective written and verbal communication. Capable of producing executive-quality narratives that survive senior leadership

review on first submission.

Bachelor's degree in Finance, Economics, Computer Science, Engineering, or related discipline.

PREFERRED

Hands-on product economics experience at a high-growth cloud, data, AI, or SaaS company.

Familiarity with consumption-based and usage-based pricing in cloud, data, or AI platforms.

Experience with competitive pricing analysis and willingness-to-pay research.

Python proficiency for data analysis. Experience with BI tools such as Tableau or Sigma.

MBA or CFA in progress or completed.

LOCATION

Menlo Park, CA. Snowflake HQ office. This role requires in-person attendance at least 3 days per week. Location is strict.

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

Roll

Analyst, Strategic Finance - Product

Plats

CA-Menlo Park, Nederländerna

Publicerad

22 juli

Sista ansökningsdag

22 augusti

Källa

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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. 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 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 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 Strategic Finance 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 * 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. 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. 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MINIMUM REQUIREMENTS * 3-5+ 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) * Familiar with fiscal year concepts and core revenue metrics (ARR, bookings, NRR) WHAT SUCCESS LOOKS LIKE AT 90 DAYS * 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 Finance Workloads team * You've participated in at least one quarterly earnings cycle * 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. 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