Senior / Staff Analyst, Tax - Finance Analytics & AI

Snowflake Snowflake · Data AI · CA-Menlo Park, United States · Data Analytics and AI

This role focuses on re-engineering core tax processes into automated, AI-first workflows using AI agents and prompt engineering. It involves building a unified data and knowledge layer, developing semantic layers, and automating tax reporting and compliance. The primary development environment is Snowflake's AI coding assistant (CoCo) and AI IDE (SnowWork).

What you'd actually do

  1. 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.
  2. Write and iterate on prompt & skill structures (YAML + Markdown skill files) based on output quality and stakeholder feedback
  3. 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.
  4. 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
  5. Participate in tax filing cycles — automating tax filings, data reconciliation, and audit-ready reporting

Skills

Required

  • LLM coding assistant usage
  • Prompt engineering
  • Python
  • SQL
  • Data modeling fundamentals

Nice to have

  • Streamlit app development
  • Semantic layer development
  • YAML
  • Markdown

What the JD emphasized

  • 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."
  • 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.

Other signals

  • AI agents
  • workflow automation
  • prompt engineering
  • semantic layer
  • data unification