Director of Data Platform Engineering

🕒 July 16

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

💵 $250k - $290k / year

⏰ Full Time

🔴 Lead

🏗️ Platform Engineer

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Owner.com

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2020

🍽️ Food & Beverage

📣 Marketing

💼 Consulting

💰 $120M Series C - Owner on 2025-05

Food & Beverage • Marketing • Consulting

Owner. com is an AI-powered SaaS platform that helps independent restaurants grow direct online sales by providing branded websites, online ordering, custom mobile apps, automated marketing, loyalty programs, and zero-commission delivery. The company emphasizes driving more Google traffic, repeat orders, and reduced third-party fees for restaurant owners through turnkey technology and support.

📋 Description

• 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.

🏖️ Benefits

• comprehensive health coverage • remote-first workplace • unlimited PTO • extra fun perks!