Data Analyst

Clay Labs•New York, NY

32d

About The Position

We’re looking for a Data Analyst to help make Clay data-driven from the ground up. You’ll join a team of experienced data scientists and analytics engineers, while partnering closely with Product and Go-to-Market teams to build foundational data models, dashboards, and analyses that teams rely on every day. This role is ideal for someone early in their data career who wants to learn quickly from senior teammates, take on meaningful ownership from day one, and do that work inside one of the fastest-growing AI startups while digging deeply into data to understand why things are happening, not just what is happening.

Requirements

Strong SQL fundamentals — comfortable with joins, CTEs, window functions, and clear query structure
Experience building analytical data models or metrics tables using dbt or similar analytics-engineering workflows
Comfortable working with BI tools like Sigma, Looker, Tableau, Mode, or similar
Strong analytical intuition — able to ask the right questions, explore data thoughtfully, and synthesize clear, well-reasoned conclusions
Clear communicator who can share insights with technical and non-technical partners
Deeply curious about product usage and business performance — motivated to go beyond the what to understand the why, without getting lost in rabbit holes

Nice To Haves

Experience with Snowflake or other similar cloud data warehousing tools
Comfortable using SQL as the primary analysis tool, with the ability to use Python or R when helpful for deeper or more efficient analysis
Familiarity with Hex or other notebook-based analysis tools
Familiarity with Census or other reverse ETL tools
Prior experience in a product-led or B2B SaaS environment

Responsibilities

Build and own analytics data models
Write clean, maintainable SQL against Snowflake to power core metrics, dashboards, and analytical workflows.
Build and iterate on analytics metrics and tables
Build and iterate on metrics and tables in dbt, and surface them through dashboards in Sigma to help teams understand product usage, performance, and change over time.
Explore data deeply to answer open-ended questions
Use SQL as your primary tool, with Python or R in a notebook environment like Hex when helpful, to investigate trends, anomalies, and product behavior and connect analyses back to real business questions.
Help operationalize data
Support reverse ETL workflows using Census, helping make modeled data actionable in the tools teams use day-to-day.
Translate questions into insight
Partner with cross-functional teams to turn ambiguous questions into clear, useful analytical outputs.