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Clay is seeking a role in its Data Platform group that builds data products used by customers and teams to search, enrich, and connect data at scale. The role emphasizes turning complex data into reliable, usable information across the product suite.
You’ll decide data meaning, model relationships, and own data serving with a focus on performance and quality. Join a team applying models and agent loops to deliver reliable data at scale.
Our mission is to help organizations turn any growth idea into reality. We see growth as a creative practice, not a formula. Finding and reaching your best-fit customers takes unique ideas and constant iteration. As AI makes execution faster and tactics easier to copy, creativity is the only lasting advantage. We’re already helping thousands of customers - including Anthropic, Notion, Google, and Ramp - go to market with unique data, signals, and AI research.
In 2025, we raised a $100M Series C backed by world‑class investors including Sequoia, CapitalG, and First Round - and crossed $100M in revenue.
In 2026, we announced our second employee tender offer in 9 months at a new $5B valuation. We also launched a community equity round , for our customers, agency partners, and club members.
Some things to know about us: Our community includes 11,000+ customers, 150+ integration partners, 125+ agencies, 50+ Clay clubs , and 30k members on Slack.
Our culture is unique inside and outside of work. Our team members are also DJs, activists, writers, clowns, marathoners, skydivers, psychedelic therapists, social workers, and more.
All employees can work for free with world‑class coaches who specialise in creativity, management, and more.
Our operating principles - including negative maintenance and non‑attached action - guide our work. Read more about them here .
Read about us in the NYT , Forbes , First Round Review , and more .
Hear from our employees directly on our Glassdoor page!
Data Platform at Clay doesn’t sit behind the product. The team builds data products customers use directly, and the shared systems that make each new one faster to build.
The first is a live map of the working world: companies, people, jobs, and how they connect. It never holds still. People change jobs, companies merge and rename, and every fact starts going stale the moment it’s captured. Customers use this map to search for companies and people, enrich records, and build the audiences they take to market.
Sources disagree about the same company or person all the time. You’ll turn that fragmented, conflicting data into reliable information customers can search, enrich, and use in their workflows, at a scale where nobody can check the answers by hand, and where a wrong answer is worse than a missing one.
The map is nowhere near finished. There are kinds of businesses it doesn’t capture well, relationships it doesn’t represent, parts of the world it covers unevenly, and changes it should notice faster. Each of those gaps could become a new data product, and anything this team ships lands in front of thousands of customers the week it launches.
Decide what the data means. Evaluate sources, resolve conflicts, and model entities and relationships the rest of the product can trust.
Explore and build new data products. Start from a customer problem, prototype a model, test it against real usage, and take it through to a product experience.
Build the shared foundation. Help define how data lands, gets modelled and versioned, how releases are evaluated, and how data reaches customers.
Own how the data is served. Support interactive search, enrichment lookups, large result sets, and alerts when a tracked company or person changes, with predictable performance.
Build with models, not just around them. More of this map is built by models reading the open web and agent loops that check each other’s work. You’ll decide when that beats a deterministic pipeline, prove it is right, and make it economical across millions of records.
Make quality a product decision. Measure coverage, correctness, freshness, and whether an answer traces back to a source. When a model makes the call, you’ll build the harness that determines whether it is good enough to ship.
Customers can ask Clay new kinds of questions and act on the answers without double‑checking them elsewhere.
A new data idea can become a product experience without rebuilding the foundation.
Quality is measured against real customer use, not just whether a pipeline is completed.
The team can change how a dataset is built without breaking the products on top of it.