Why we're looking for youWe are hiring a Head of Data Platform Engineering to own the platform that turns that data into something the company and our products can rely on. This is a hands-on engineering leadership role: you will set the architecture, build the hard parts yourself early on, and grow a team behind you. The remit runs the full path, from moving data out of production systems into a trusted warehouse, to modeling and governing it, delivering it reliably, and serving it to analysts, product features, and our ML systems. The job is to build that platform, not to bolt an "AI layer" onto something that does not exist yet.
This role is remote and can be based anywhere in the US or Canada. For San Francisco-based candidates: we have a beautiful office in the Presidio for optional hybrid collaboration opportunities.
The impact you'll have- Own the end-to-end data platform architecture from source systems to serving, deciding where the stack (today Snowflake, Fivetran, dbt, Sigma, and Hex) consolidates, where it needs rebuilding, and whether it should move toward a lakehouse pattern.
- Design the path for order data as it moves off the legacy monolith onto a new source of truth, from event consumption and change data capture through to projections, so it lands in the warehouse correctly and on time.
- Bring real modeling rigor and a single semantic layer so core metrics like revenue, conversion, and retention carry one definition everywhere, then make testing, monitoring, and SLAs standard so issues surface before stakeholders see them.
- Provide the feature and serving infrastructure that lets restaurant ML run safely and reproducibly, and manage warehouse cost and performance as a first-class engineering metric.
Who you'll work with- Engineering leadership and the VP of Business Operations, plus the orders and payments teams as that domain moves to its new source of truth.
- BizOps and GTM analytics, Product, Finance, and the data scientists and applied-AI engineers who build on the platform you own.
What we're looking for- Roughly 10 or more years in data engineering, data platform, or analytics engineering, including 3 to 5 years leading and building teams that other people depend on.
- You treat pipelines like software: version control, code review, automated tests, schema and data contracts, and CI/CD, with changes reviewed before they reach production.
- Deep hands-on experience with a modern data stack: a cloud warehouse or lakehouse (Snowflake, Databricks, BigQuery, or Redshift), dbt or similar transformation tooling, orchestration (Airflow, Dagster, or similar), and both batch and streaming (Kafka, Spark, Flink, or equivalents).
- Strong data modeling and SQL plus production-quality Python, with proven ownership of data reliability through SLAs, SLOs, quality monitoring, incident response, and root-cause work.
- Practical governance experience across lineage, cataloging, and access control, including PII and payments data, and good judgment about where AI and machine learning belong and where they add risk.
Compensation and benefits- The estimated base salary range for this role is $250-290K, plus a generous pre-IPO equity package
- Other benefits include comprehensive health coverage, remote-first workplace, unlimited PTO - plus extra fun perks!
