• Own Mixbook’s data platform end-to-end, including ingestion, orchestration, transformation, and the Redshift data warehouse • Establish clear accountability, SLAs, and standards for critical datasets and business metrics • Stabilize and harden fragile pipelines, reducing data incidents and reactive firefighting • Define and enforce best practices for dbt modeling, data quality, and metric consistency. Deliver analytics-ready models that power Looker and downstream use cases. • Partner closely with Platform Engineering, Product Engineering, Data Analytics, and business stakeholders across Marketing, Product, Operations and Finance to align on priorities and tradeoffs • Act as the primary point of contact for data reliability and warehouse health at the executive level • Make pragmatic build vs. buy decisions and resist unnecessary replatforming or tool churn • Lead by building. Write and review production data engineering code. Set technical standards and architectural direction. • Enable faster, more confident analytics and experimentation by increasing trust in data • Set the stage for future data science, ML and AI capabilities once foundations are solid