AI Analytics Engineer

Clay Labs•New York, NY

About The Position

We're looking for an AI Analytics Engineer to scale Clay's most critical data system: the Revenue + Cost + Margin Engine - the single source of truth that powers every team (finance, sales, product, exec). You'll join a small, high-leverage data team and work with novel data: accurate workspace-level AI margins that enable decisions no other company can make. This role is ideal for someone who thinks in systems, not dashboards. You're fluent in the modern data stack (dbt, Snowflake, Streamlit) and already using AI tools (Cursor, MCPs, LLMs) to move 5x faster than traditional methods. The foundation is set; your job is to scale it into a lean, streamlined workhorse.

Requirements

Mastery of the modern data stack: Fluent in dbt, Snowflake, Streamlit, and the surrounding ecosystem. You know how to design data models that scale.
AI-native workflows: You're already using AI tools (Cursor, MCPs, LLMs) to write code, document models, and solve technical hurdles.
Systems thinking: You think in terms of data flows, sources of truth, and business logic - not just dashboards or reports.
Understanding of how businesses consume data: You're curious about how different teams view things, use data, and could use AI to 10x their workflows.
Financial precision: You understand that while AI helps you move fast, you are the final auditor. You take pride in the integrity of the numbers.
High agency: You don't need a roadmap. You find the biggest, ugliest data problem and you solve it.

Nice To Haves

Experience with consumption-based revenue models or complex pricing logic
Familiarity with MCPs (Model Context Protocol) or similar AI tooling
Prior experience in a high-growth SaaS environment
Understanding of financial reporting requirements (though not required - we'll teach you)

Responsibilities

Build and understand the warehouse of data
Scale the Revenue + Cost + Margin Engine (every closed-won deal, every dollar of revenue, every dollar of cost, and the margins that result)
Architect the data models that power the entire company - finance, sales, product, exec all depend on this
Work with novel data: accurate workspace/account-level margins for AI consumption
Ensure the foundation is audit-ready and immutable (financial precision, not "pretty good")
Understand how the org consumes information
Learn how different parts of the business view things, use data, and could use AI to 10x their workflows
Design systems that align with how teams actually work, not how we wish they worked
Build quick views using AI and Streamlit - only create dashboards for what matters
Design and implement systems to scale information flow
Build AI-native infrastructure (MCPs, Cursor workflows) that makes the warehouse instantly accessible
Enable anyone to query the database, read Notion docs, and post to Slack through ChatGPT or Cursor
Design the data flows and information architecture that keep the company aligned as it scales 10-100x