Senior Staff Analyst (Tax - Finance Analytics & Artificial Intelligence)

Join our AI-first analytics team as a Senior Staff Analyst in Tax - Finance Analytics & Artificial Intelligence. In this role, you will partner closely with Tax leadership to transform their function using AI, build a unified data and knowledge layer for tax-relevant information, and automate compliance reporting. You will work in an AI-IDE, write Python applications, and communicate complex ideas simply to stakeholders. This position offers comprehensive health insurance, retirement plans, and generous time-off.

• Partner directly with Tax leadership to re-engineer core tax processes into automated, 'AI-first' workflows.
• Build a unified data and knowledge layer that serves as a single source of truth for all tax-relevant information.
• Take ownership of the tax compliance and risk analysis workflows, ensuring they run correctly on schedule without hand-holding.
• 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
• 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 think in terms of context, instructions, examples, and output format — not just "the thing I typed before the code came out."
• You don't look up the syntax for a row-numbered deduplication
• A non-technical user should be able to query your model in plain English and get a correct answer
• SQL — CTEs, window functions, incremental pipeline patterns
• You write Python-based applications, data pipelines, and reporting automation
• 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
• 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
• You understand caching, session state, and how to structure a multi-page app cleanly
• Python — Modern, type-hinted, readable
• 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
• Finance literacy — You can read a revenue waterfall, distinguish ARR from NRR, and explain what drives a QoQ change in product revenue
• 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
• 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
• 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
• 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
• You communicate complex ideas simply, ensuring stakeholders understand, trust, and can act on what you build
• Works fast with high accuracy
• 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 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
• Translates between AI, data, and tax
• Thinks in workflows, not tasks
• Familiar with fiscal year concepts and core revenue metrics (ARR, bookings, NRR)
• 5+ years of experience in analytics, data engineering, or a technical finance adjacent role
• Has shipped multiple Python applications that end-users actually interacted with; at least one is actively maintained in production
• Proficient in SQL — you can write a window function without looking it up
• Has used an AI coding assistant as a primary development tool — daily usage, not occasional
• Comfortable working in Git (PRs, branches, code review)