Remote
US
$140K–$140K
💼Sales
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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.
Location: This is a hybrid role in Menlo Park, CA
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.
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.
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.
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.
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.
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.
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
USD 114,000 - 150,100
Annually
Health Insurance
Dental Insurance
Vision Insurance
Pet Insurance
Life Insurance
Disability Insurance
Health Savings Account (HSA)
Flexible Spending Account (FSA)
Mental Health Benefits
Employee Assistance Program
Fitness Stipend
Wellness Programs
401(k) / Retirement Plan
Employee Stock Purchase Plan
Equity / RSUs
Performance Bonus
Parental Leave
Adoption Leave
Fertility Benefits
Unlimited PTO
Learning & Development Budget
It is structured hybrid. Most roles are tied to a specific office and expect roughly three days a week on-site, and around three-quarters of open jobs are office-anchored. A minority of roles are explicitly tagged remote, but there is no company-wide remote policy, so eligibility is decided role by role.
Roles are location-specific and spread across more than 25 countries, including the US, UK, Australia, India, Germany, Poland, Canada, Japan, France, and Singapore. The heaviest concentration of jobs sits in US hubs such as Menlo Park and Bellevue, plus London. Positions are city- or country-locked rather than work-from-anywhere.
Snowflake offers a 401(k) and a Roth 401(k) option, but it does not provide an employer match. This is worth factoring into total compensation, since the package leans heavily on equity and bonus rather than retirement matching.
Compensation is geography-based and certified by Fair Pay Workplace, an external pay-equity auditor. Cash is paired with substantial equity through new-hire grants, an Employee Stock Purchase Plan, and a quarterly bonus or commission program, with RSUs vesting over four years.
Highlights include unlimited PTO, generous parental leave (26 weeks maternity, 12 weeks paternity) with adoption and fertility support, an HSA with employer contribution, on-demand mental health and wellness programs, and ergonomic work-from-home equipment provided globally.
It suits data and AI engineers, data scientists, and product people who want to work across the full data lifecycle at large scale and value equity-heavy pay. Sales roles are numerous and well-compensated, though reviewers there report more pressure and crunch than in engineering.
Pay Transparency
Commuter Benefits
Sick Days
Military Leave
Paid Holidays
Bereavement Leave
Company Equipment
Free Snacks & Drinks
Employee Product Discount
Employee Resource Groups
