Job Title: Data Engineer

Company Name: AirOps

Job Url: https://jobs.ashbyhq.com/airops/d672079b-ae31-4b41-a0b7-0ea4121a9313?utm_source=jobright&jr_id=69b6365906c1ba00c5480c84

Job Description: Why this role, why now

Our product is data. Customers like Webflow, Ramp, and Carta rely on AirOps to understand exactly how they show up across AI search, and that data has to be fast, accurate, and trusted. Until now, data engineering has lived inside the broader engineering team. We've outgrown that. As our product becomes more data-intensive, we need someone who owns this layer end to end, not because it's in their job description, but because they won't have it any other way. This is the foundational data hire at AirOps, and it's one of the most important roles we're filling this year.

What you'll do

Own the data pipelines that power customer-facing analytics. You define what done means, you ship it, and you stand behind it

Design and maintain 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

Work directly with product and engineering to ship data-powered features. You move fluidly between a product spec and a query plan without losing momentum or waiting to be told what the next problem is

Build enrichment pipelines that shape raw data into the derived entities our product depends on. You go beyond the ask when you see a better path

Set the data engineering foundation as the first dedicated hire in this function, working closely with our VP of Engineering. You build for what the team will need, not just what's asked of you today

Who you are

You think like a backend engineer who works closer to the data layer. When someone asks who your users are, you talk about customers, not analysts, and you take it personally when what they see is wrong or slow

You've shipped systems where the output lands directly in a product that external users interact with, not an internal dashboard, not a BI report

Strong in 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

You own things without being asked and drive them to closure. Scope doesn't constrain you, outcomes do

You have the range to hold your own in a technical architecture discussion and ship the thing the same week

5+ years of hands-on engineering experience with clear evidence of owning a data-powered product surface