Analytics Lead

Snorkel AI Snorkel AI · Data AI · Redwood City, CA +1 · 532 - Finance Ops

This role focuses on building and maintaining AI-first analytics infrastructure, including semantic layers, LLM tooling, evals, and agents, to empower teams within an enterprise. It involves defining ontologies, architecting models, and identifying high-leverage data opportunities. The role requires strong SQL, Python, and modern data stack experience, with a focus on high-autonomy and stakeholder partnership.

What you'd actually do

  1. Own and build out our AI-first analytics infrastructure end-to-end: semantic layer, LLM tooling, evals, and agents that analytically empower every team in the company.
  2. Maintain this AI-first analytics infrastructure so it doesn’t decay as the business grows.
  3. Define and implement the taxonomy and domain ontology to match Expert Contributors to projects and tasks at scale. This is foundational to our marketplace business model.
  4. Architect a demand and supply model that gives leadership a shared, forward-looking view of where we're exposed and where to invest across domains, geographies, and acquisition channels.
  5. Identify the next highest-leverage opportunities where the Data Team can address a business-limiting constraint and help prioritize them.

Skills

Required

  • 5+ years in data roles spanning analytics engineering, analytics, and data science
  • SQL fluency
  • strong Python skills
  • familiarity with the modern data stack (e.g. Fivetran, dbt)
  • hands-on Snowflake or comparable cloud warehouse experience
  • High-autonomy IC track record
  • Hands-on experience with modern AI tooling: LLMs, semantic layers, eval frameworks, and the infrastructure work required to put these into production.
  • Experience identifying high-leverage opportunities in startups without a fully defined analytics agenda and comfort in going from 0-to-1 in high-growth environments.
  • Genuine enjoyment of stakeholder partnership

Nice to have

  • Streamlit
  • Snowflake Cortex
  • familiarity with AI/data labeling
  • DaaS
  • marketplace business models
  • experience building semantic layers or BI infrastructure
  • A/B testing in marketplace or workforce platform contexts

What the JD emphasized

  • AI-first analytics infrastructure
  • LLM tooling
  • evals
  • agents
  • semantic layer
  • taxonomy and domain ontology
  • High-autonomy IC track record
  • Hands-on experience with modern AI tooling

Other signals

  • AI-first analytics infrastructure
  • LLM tooling
  • agents
  • semantic layer
  • taxonomy and domain ontology