Analyst/ Senior Analyst - Legal Analytics & AI

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

This role focuses on building AI-powered conversational analytics experiences for legal teams using Snowflake Cortex. The responsibilities include owning the data layer, logic, and quality of these AI experiences, translating stakeholder needs into working products, delivering actionable insights, and maintaining rigor through testing and validation. The role also involves contributing to access and security design for sensitive legal data.

What you'd actually do

  1. Build AI-powered experiences for Legal teams
  2. Co-own the data foundations with Analytics Engineering
  3. Translate stakeholder needs into working products
  4. Deliver actionable legal insights
  5. Maintain rigor as we scale

Skills

Required

  • 3+ years of experience in analytics, data engineering, or a related field where you've owned data products end to end
  • Strong SQL — you write complex queries and think carefully about performance, grain, and correctness
  • Familiarity with the modern data stack: tools like dbt, Airflow, and Git as part of a collaborative engineering workflow
  • Experience working directly with non-technical stakeholders — you can translate a business question into a working data model and explain your thinking without jargon
  • Data quality instincts — you validate your work against source systems and don't ship without checking your numbers
  • Curiosity about AI and where it's going — you don't need to have shipped an LLM product, but you should be excited about building them

Nice to have

  • Hands-on experience with Snowflake Cortex, LLM-powered analytics tools, or building conversational data experiences
  • Familiarity with Streamlit for building lightweight internal apps and dashboards
  • Exposure to Legal Operations data — matter management, legal spend, or outside counsel analytics
  • Python experience for data pipelines, scripting, or automating reporting workflows
  • Experience designing access controls for sensitive data: privileged legal information and confidential business data

What the JD emphasized

  • owning the full stack
  • share ownership of getting that right
  • give teams something they can use immediately
  • stakeholder doing something different because of what you showed them
  • making sure outputs stay aligned with source systems
  • ensure the right access controls are in place
  • secure views, row-level policies, and data sensitivity controls

Other signals

  • AI-powered experiences
  • conversational analytics
  • data-grounded answers
  • full stack ownership
  • data foundations
  • stakeholder needs
  • actionable insights
  • quality checks
  • access and security design