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New York, New York, United States
Hybrid
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 specialize 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 modeled 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.
You have a proven track record of execution. You have 8+ years of hands-on engineering experience building products customers rely on.
You love being a product manager as well as an engineer. Our product engineers drive and own products from end to end.
You've led ambiguous, cross-functional technical work from problem framing through delivery, creating clarity and scope for others along the way.
You are an empathetic communicator. You express nuanced ideas clearly at different levels of abstraction for different audiences. In disagreements, you prioritize curiosity over confrontation, making sure everyone feels heard and understood.
You love to collaborate with others to ship high quality, thoughtful features. You talk to customers, consider multiple approaches, and work with teammates to find the right solution. You care about craft, write clear and maintainable code, and create frameworks and reusable patterns that eliminate classes of problems, not just point fixes.
Having a diversity of perspectives is important to you. You believe that having people with different backgrounds and perspectives creates a better team and a more holistic product.
You’re familiar with our current tech stack or can learn unfamiliar technologies quickly. You don't need experience with every technology listed:
Product stack: React, TypeScript, Python, Node.js, Aurora PostgreSQL, Redis, ECS Fargate, Lambda, OpenSearch
Data platform: Python, SQL, Iceberg on S3 with Glue and Athena, SQLMesh, ClickHouse, Airflow, Terraform
Observability: Datadog, CloudWatch
You've built and operated production systems where data quality and reliability both matter.
You're comfortable in SQL and Python, and can reason about data models, distributed jobs, lineage, and failure recovery.
When a customer gets a wrong answer, you treat it as your bug, wherever it came from.
When datasets disagree or the right quality bar isn't obvious, you measure the problem and improve from evidence. You'd rather bring that evidence to Product or Data Science than argue positions.
Experience with entity resolution, graph data, or temporal data.
Experience putting an LLM in the critical path of a shipped product, including how you proved it was correct.
Experience with search infrastructure or data-intensive product systems.
Experience evaluating and operating on large third-party datasets.
This isn't an analytics, BI, or warehouse-enablement role, and it isn't pure infrastructure. The team owns whether each data product is correct, useful, and fast enough for customers.