Head of Data Platform Engineering

Owner is hiring a Head of Data Platform Engineering to build and lead the data platform from production data sources through warehousing, modeling, governance, and serving. The role will set the architecture, implement core platform components, and support analytics, product features, and machine learning systems.

Responsibilities:

• 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

Requirements:

• 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