Senior / Staff Software Engineer, Data Platform

About AirOps AirOps is the first end-to-end content engineering platform built for the AI era. In a world where discovery is shifting from traditional search to AI-driven platforms, we help brands get found—and stay found. We are currently in a phase of hyper-growth, having 5x’d our revenue in the last year by helping marketing teams at Ramp, Chime, Carta, and Rippling turn content quality into a durable competitive advantage. Our platform equips marketers to navigate the new discovery landscape, prioritize high-impact opportunities, and create accurate, on-brand content that earns citations from AI and trust from humans. Backed by Greylock, Unusual Ventures, Wing VC, and Founder Collective, we are building the intelligent systems that will empower the next generation of marketing leaders. AirOps is headquartered in San Francisco, New York and Montevideo. Why this role, why now These are some of of the most important technical hires we’ll make this year. AirOps customers rely on us to understand exactly how they show up across AI search — and that data has to be fast, accurate, and trusted. We’ve outgrown “data engineering as a side quest.” These are foundational Data Platform hires at AirOps. You’ll own the layer end-to-end: pipelines, serving, and the guarantees that make customer-facing analytics feel rock solid. If you want true ownership over a data product surface that external users depend on — and the latitude to build it the right way — you’ll be excited about this role. What you'll do The data pipelines that power customer-facing analytics: you define what done means, you ship it, and you stand behind it The serving layer that delivers citation rates, share of voice, and mention trends to customers across ChatGPT, Perplexity, Gemini, and beyond — with strong guarantees on accuracy, freshness, and latency Enrichment pipelines that shape raw data into the derived entities the product depends on — you go beyond the ask when you see a better path Direct collaboration with product and engineering to ship data-powered features: you move fluidly between a product spec and a query plan without losing momentum The data engineering foundation itself: you build for what the team will need, not just what is asked of you today Who you are 5+ years of hands-on engineering experience with clear evidence of owning a data-powered product surface that external users interact with — not an internal dashboard, not a BI report Strong Python and SQL with hands-on experience in ClickHouse, Redshift, or similar OLAP systems at product scale You know the difference between a query that works and one that holds up under real customer load The range to hold your own in a technical architecture discussion and ship the thing the same week Ownership mentality: scope does not constrain you, outcomes do Nice to have Experience at a company where data is the product: Propel, Tinybird, Hex, Amplitude, Mixpanel, or similar Familiarity with the AWS-native stack: Glue, S3, Redshift Experience integrating LLMs into data pipelines for enrichment, classification, or tagging Our Guiding Principles Extreme Ownership Quality Curiosity and Play Make Our Customers Heroes Respectful Candor Benefits Equity in a fast-growing startup Competitive benefits package tailored to your location Flexible time off policy Parental Leave A fun-loving and (just a bit) nerdy team that loves to move fast!

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