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Hazel, based in New York City with a hybrid work model, seeks a Data Platform Engineer to own the entire data stack—from ingestion to orchestration. You will develop AI agents to build and test integrations end-to-end and automate key workflows to scale the team’s impact.
Ideal candidates have 4+ years of production data infra experience, strong SQL and Python skills, and a track record with orchestration tools like Dagster/Airflow/Temporal.
Hazel is the AI coworker for consumer brands. We connect to a brand's live data - Shopify, Klaviyo, Amazon, Meta, and dozens of others - and turn it into a teammate operators talk to every day. Ask \"why did repeat purchase rate drop last week?\" and 10 seconds later you get an answer, cited from live data.
Hazel also takes action herself, proactively flagging issues and making changes directly in Shopify and the other systems she connects to, on the way to running full playbooks autonomously.
We've raised more than $2M, are rapidly scaling and serving some of the biggest consumer brands including Bogg Bag, Ultra, and OneSkin.
Hazel's intelligence is built on top of a data warehouse meticulously tailored for consumer brands. We're moving a massive amount of data and the owner of this layer plays a direct part in how users interact with Hazel.
Our data layer is tens of terabytes of data and thousands of data tables, and customers expect perfect sync reliability, new integrations to be built in days, and for every question Hazel asks to be correctly grounded in data.
Experience with our stack is a bonus, but similar experience working on problems at our scale is required: dbt, Temporal, dltHub, Duckdb / Motherduck, Dagster.
You own the data platform end to end - ingestion, transformation, orchestration, and the reliability of all three. As importantly, you'll need to further develop our existing AI agents that write, build, and test new integrations end-to-end. Automating your job is the only way you will scale it.