Staff Data Platform Engineer

Data Platform Engineer (Customer-Facing Analytics)Location: (SF / NYC / Remote — confirm preference)Compensation: $220,000 - $250,000 + equityTech Stack: Python, Postgres, Clickhouse, AWS, Kafka, Spark, Airflow.About Our ClientOur client is building an end-to-end content engineering platform for the AI era. As discovery shifts from traditional search to AI-native platforms, they help brands get found—and stay found—by turning content quality into a durable competitive advantage. They’re in hyper-growth and support high-performing marketing teams at leading companies.The team is backed by top-tier investors and operates across major hubs including San Francisco and New York (with additional global presence). Their culture emphasizes extreme ownership, quality, curiosity, and making customers heroes.Why This Role, Why NowThis is one of the most important technical hires our client will make this year. Customers rely on the product to understand how they appear across AI search surfaces—and the underlying data must be fast, accurate, and trusted.They’ve outgrown “data engineering as a side quest.” This is a foundational Data Platform role where you’ll own the customer-facing data layer end-to-end: pipelines, serving, and the guarantees that make analytics feel rock-solid.If you want true ownership over a data product surface external users depend on—and the latitude to build it the right way—this role is for you.The RoleOur client is hiring a Data Platform Engineer to build and scale the data systems powering customer-facing analytics like citation rates, share of voice, and mention trends across AI-driven platforms (e.g., ChatGPT, Perplexity, Gemini, and others).This role blends product-minded engineering with deep technical execution. You’ll collaborate directly with product and engineering, moving fluidly from specs to query plans to production systems.What You’ll DoOwn the data pipelines powering customer-facing analytics: define what “done” means, ship it, and stand behind itBuild the serving layer that delivers metrics with strong guarantees on accuracy, freshness, and latencyDevelop enrichment pipelines that convert raw inputs into derived entities the product depends on (classification, tagging, canonicalization, etc.)Partner closely with product and engineering to ship data-powered features—fast and with high qualityEstablish the data engineering foundation the team will need as the company scales (tooling, standards, performance practices, observability)What Our Client Is Looking ForRequired5+ years of hands-on engineering experience with clear evidence you’ve owned a data-powered product surface that external users interact with (not internal dashboards/BI-only work)Strong Python and SQLHands-on experience with OLAP systems at product scale (e.g., ClickHouse, Redshift, or similar)Strong performance instincts: you know the difference between a query that works and one that holds up under real customer loadThe range to contribute to architecture decisions and still ship meaningful improvements the same weekHigh ownership mentality: you optimize for outcomes, not narrow scopeNice to HaveExperience at “data is the product” companies (e.g., analytics platforms, data serving products)Familiarity with AWS-native stacks (Glue, S3, Redshift)Experience integrating LLMs into pipelines for enrichment, classification, tagging, or extractionGuiding Principles (Culture Fit)Extreme OwnershipQualityCuriosity and PlayMake Our Customers HeroesRespectful CandorBenefitsEquity in a fast-growing startupCompetitive benefits package tailored to locationFlexible time offParental leaveA fun-loving (and slightly nerdy) team that moves fast