Senior / Staff Data Engineer

AirOps•San Francisco, CA

10h

About The Position

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. About the Role AirOps sits at the intersection of AI and brand intelligence, processing millions of AI-generated answers every day across every major AI provider. The data platform that powers that is growing fast: 2 million answers processed daily, expanding by hundreds of thousands each month, with new providers and new product capabilities coming online constantly. This is the kind of scale where the data engineering decisions you make today have direct product consequences tomorrow. You will own the pipelines, ingestion infrastructure, and data quality systems that sit underneath everything customers see. We are building a dedicated data team for the first time, which means the patterns you establish will define how this function operates for years. You will work across ClickHouse, distributed ingestion pipelines, and a multi-provider scraping stack, with real ownership over reliability and correctness. If you want to work on data that is the product, not just internal tooling, this is that role.

Requirements

Built and maintained production data pipelines at meaningful scale, including experience with ingestion, ETL, and distributed job queue systems
Hands-on experience with ClickHouse or a comparable columnar store, including schema design, materialized views, and query optimization
Experience working with third-party data providers or scraping infrastructure at scale
Designed and owned reliability and observability systems: you have built the dashboards and alerting that tell you what is wrong before users do
Comfortable working close to the application layer: our stack is Rails and Postgres and this person will need to understand how backend systems feed the data platform

Nice To Haves

Familiarity with AI provider ecosystems (ChatGPT, Gemini, Perplexity) and how answer formats affect extraction and processing
Experience migrating synchronous processing to batch or async patterns
Prior work at a B2B SaaS company where data accuracy was a customer-facing product requirement

Responsibilities

Reliability and completeness of our multi-provider ingestion pipeline: build failover logic, track daily completion rates per AI provider, and maintain the systems that ensure customers always get accurate, timely data
Data quality and consistency end to end: own the contracts between ingestion, processing, and downstream serving so that what customers see in the product is correct and trustworthy
Extraction pipeline performance: design and maintain async processing patterns that keep our ingestion throughput ahead of demand as we scale
Data readiness for new features: partner with engineering upstream so instrumentation, schema changes, and pipeline capacity are in place before features ship
Pipeline observability: build and maintain health monitoring across the full data stack so the team knows the state of the system in real time