Why This Job is Featured on The SaaS Jobs
Snowflake sits squarely in the modern SaaS infrastructure layer, and this role highlights a growing pattern inside leading subscription businesses: internal finance functions being rebuilt with product-grade analytics and AI. Rather than traditional reporting, the work centers on agentic workflows, semantic layers, and lightweight applications that operational teams can use directly, which reflects how mature SaaS companies are treating data and automation as core systems.
For a SaaS career, the value is in learning how recurring-revenue organizations turn governance-heavy domains into scalable, auditable pipelines. Experience here maps to other SaaS environments where accuracy, traceability, and self-serve analytics matter, especially as AI-assisted development becomes a standard expectation. The combination of Python, SQL, modeling discipline, and human-in-the-loop evaluation is broadly transferable across revenue operations, finance analytics, and data product roles.
This is a strong match for professionals who prefer building reusable systems over one-off analyses and who are comfortable being the translation layer between domain stakeholders and technical implementation. It suits someone who enjoys shipping small, maintainable artifacts on a regular cadence and iterating based on real usage, particularly where correctness and clear stakeholder communication are central to success.
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 Type: 3 Days in Menlo Park Office
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 Analytics team builds the intelligence layer that the Tax function under the CFO office runs on: AI agents that encode repeatable tax 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. You will partner closely with Tax leadership to transform their function using AI. 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 focused on unifying fragmented tax data, identifying high-risk areas, and automating compliance reporting to allow the Tax team to focus on decision-making and exception handling. One week you're building a new AI agent for tax risk identification; the next you're designing a compliance reporting tool. You are equally comfortable in an AI-IDE, a Python file, and a stakeholder summary for a senior tax leader.
What you'll work on
AI agent and workflow development (primary focus)
AI Agent & Workflow Transformation: Partner directly with Tax leadership to re-engineer core tax processes—including compliance, risk identification, and global reporting—into automated, 'AI-first' workflows. Design and deploy agentic tools using CoCo and CoWork that reduce manual data gathering, allowing the team to shift focus from data preparation to strategic decision-making and exception handling.
Write and iterate on prompt & skill structures (YAML + Markdown skill files) based on output quality and stakeholder feedback
Finance analytics
Tax Intelligence & Unification: Build a unified data and knowledge layer that serves as a single source of truth for all tax-relevant information. Transform fragmented data sources into clean, reconciled datasets, and create an 'AI tax brain' that encodes tax laws, internal playbooks, and regulatory updates to enable instant, accurate analysis across domestic and international tax workflows.
Support risk assessment models and compliance reporting pipelines
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 tax cycle
Develop and deploy production tax 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
Tax reporting and compliance automation
Participate in tax filing cycles — automating tax filings, data reconciliation, and audit-ready reporting
Build and maintain source-of-truth reporting exports (multi-tab Excel, formatted to spec)
Support ad-hoc disclosure and tax audit data needs
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 tax 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 tax
Your stakeholders are tax analysts and directors who think in spreadsheets and compliance filings. 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 the tax function 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
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 tax compliance and risk 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 Tax leadership team
You've participated in at least one tax compliance or filing 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. 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
