{"id":58954,"url":"https://alion.io/job/clay-software-engineer-data-products-platform","title":"Software Engineer, Data Products & Platform","company":{"id":1484,"name":"Clay","domain":"clay.com","url":"https://alion.io/company/clay","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":84,"open_postings":9,"ghost_share":0,"stale_share":0.444,"repost_share":0,"time_to_fill_p50_days":75,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":170000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon ECS","optional":true},{"name":"Amazon S3","optional":true},{"name":"ClickHouse","optional":true},{"name":"Datadog","optional":true},{"name":"JavaScript","optional":true},{"name":"LLM","optional":true},{"name":"Node JS","optional":true},{"name":"OpenSearch","optional":true},{"name":"PostgreSQL","optional":true},{"name":"React.js","optional":true},{"name":"Redis","optional":true},{"name":"Slack","optional":true},{"name":"Terraform","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-08-14T21:44:33Z","employer_posted_date":"2026-08-14","last_verified_at":"2026-11-01T09:59:33Z","board_verified":true,"closed_at":null,"days_open":78,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":78},"description":"About Clay\nAI is unleashing the biggest wave of company creation in history. Clay's mission is to be the engine those companies use to grow to their full potential.\nClay predicts the next best action for your business and then helps you take it. We started by aggregating the best data for B2B companies. Then, we built the infrastructure to run any personalized campaign on top of it, including emails, ads, landing pages, and workflows that make reps more productive. Now, we're building agents that can help grow your company for you. We're already helping thousands of customers - including Anthropic, OpenAI, Google, and Visa - go to market with unique data, signals, and AI research.\nIn 2026, we raised a $115M Series D at $7.1B Valuation led by Wellington - and are on track to cross $200M in revenue this quarter. We also launched a $1M Scholarship fund for GTME Education.\nSome things to know about us:\nOur community includes 17,000+ customers, 200+ integration partners, 125+ agencies, 50+ Clay clubs, and 30k members on Slack.\n\nOur 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.\n\nAll employees can work with world-class coaches who specialize in creativity, management, and more.\n\nOur operating principles - including negative maintenance and non-attached action - guide our work. Read more about them here.\n\nRead about us in the NYT, Forbes, First Round Review, and more.\n\nHear from our employees directly on our Glassdoor page!\nThe Role\nData 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.\nThe 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.\nSources 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.\nThe 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.\nWhat You'll Work On\nDecide what the data means. Evaluate sources, resolve conflicts, and model entities and relationships the rest of the product can trust.\n\nExplore 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.\n\nBuild the shared foundation. Help define how data lands, gets modeled and versioned, how releases are evaluated, and how data reaches customers.\n\nOwn 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.\n\nBuild 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.\n\nMake 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.\n\nWhat Success Looks Like\nCustomers can ask Clay new kinds of questions and act on the answers without double-checking them elsewhere.\n\nA new data idea can become a product experience without rebuilding the foundation.\n\nQuality is measured against real customer use, not just whether a pipeline is completed.\n\nThe team can change how a dataset is built without breaking the products on top of it.\n\nWhat You'll Bring\nYou have a proven track record of execution. You have 8+ years of hands-on engineering experience building products customers rely on.\n\nYou love being a product manager as well as an engineer. Our product engineers drive and own products from end to end.\n\nYou've led ambiguous, cross-functional technical work from problem framing through delivery, creating clarity and scope for others along the way.\n\nYou 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.\n\nYou 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.\n\nHaving 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.\n\nYou’re familiar with our current tech stack or can learn unfamiliar technologies quickly. You don't need experience with every technology listed:\nProduct stack: React, TypeScript, Python, Node.js, Aurora PostgreSQL, Redis, ECS Fargate, Lambda, OpenSearch\n\nData platform: Python, SQL, Iceberg on S3 with Glue and Athena, SQLMesh, ClickHouse, Airflow, Terraform\n\nObservability: Datadog, CloudWatch\n\nYou've built and operated production systems where data quality and reliability both matter.\n\nYou're comfortable in SQL and Python, and can reason about data models, distributed jobs, lineage, and failure recovery.\n\nWhen a customer gets a wrong answer, you treat it as your bug, wherever it came from.\n\nWhen 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.\n\nNice To Haves\nExperience with entity resolution, graph data, or temporal data.\n\nExperience putting an LLM in the critical path of a shipped product, including how you proved it was correct.\n\nExperience with search infrastructure or data-intensive product systems.\n\nExperience evaluating and operating on large third-party datasets.\n\nOut of Scope\nThis isn't an analytics, BI, or warehouse-enablement role, and it isn't pure infrastructure. 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