Backend Engineer, Data

THE ROLE
A warehouse full of consumer data does nothing for a marketer until they can sort, filter and chart it in the time it takes to ask the question.

Our data team builds the consumer graph and each brand's first-party models, and our data scientists train scores on top. This seat makes all of it usable in the product: first-party fields that respond instantly, scores on the pages marketers already use, segments that stay current, and an agent that turns a plain-English question into the right query against a brand's own data.

This is an IC or senior IC seat on product engineering, working day to day with data engineering and data science. Your counterpart on the warehouse side is our Staff Data Engineer, who owns the golden model and publishes the semantic views you build on.

WHAT YOU'LL DO
Own how first-party data performs in the app. Any field a brand connects can be sorted, filtered and charted without a live warehouse query. You decide what gets precomputed, where it lives and how it stays fresh.

Own how our models reach marketers. Propensity, LTV, churn and product-fit scores on the person, segment and product pages, with full distributions instead of a single number.

Own dynamic segments. Segments that update as new data lands, with stats ready for activation.

Own the in-app agent's query tool, built on the semantic views our data platform team publishes. You own the path from a marketer's question to the right rows.

Own the product side of the sync. What gets precomputed, and the path from Postgres to the synced client. Each hop costs money and time, and you choose on purpose.

Extend the pipelines. You mostly build on what the data team produces. When a feature needs a table that doesn't exist, you write the model or pipeline step yourself.

Ship end to end. One feature can mean a Python Lambda, a Zero schema change and a TanStack Start page. You ship all three.

OUR STACK
App: TypeScript, TanStack Start, React 19, Tailwind and Base UI, on Vite and Bun.

Sync: Zero on Postgres, schema in Drizzle. The app reads from a live-synced local copy of the data instead of request-response endpoints.

AI: Mastra for agents, Temporal for long-running work.

Backend: Python on AWS Lambda.

Data: Snowflake, including semantic views and Cortex.

WHAT YOU HAVE
Production experience with a cloud data warehouse. Snowflake preferred.

Production TypeScript, and backend services shipped on AWS: Lambda or similar.

You're current. You know what shipped in AI tooling this month and what changed in the latest release of the packages you depend on.

You build things on your own time, and you can show us.

You're in New York City or willing to relocate.

YOU MIGHT BE A FIT IF YOU ARE…
A full-stack engineer at a data-heavy SaaS company who ended up owning the warehouse because someone had to, and liked it.

A product engineer at a warehouse-native analytics or activation company who has spent years making Snowflake feel fast inside an app.

A backend engineer who has shipped on a sync engine like Zero, Replicache or ElectricSQL, and wants a dataset big enough to stress it. We'd be especially impressed by this one.

A data engineer tired of handing tables to other teams who wants to own the feature the table ends up in.

Or you don't fit any of these neatly, but you built something this year that changed how you think data should reach a user. Apply.

WHAT SUCCESS LOOKS LIKE
First 90 days: new scores and first-party tables reach the app through a repeatable system you built.

First 6 months: any first-party field can be sorted, filtered and charted without a live warehouse query, and the in-app agent's query tool runs on your layer.

No single number. When product engineering has a data question, it comes to you.