Engineering Manager, Data Modeling

Key Highlights

• Own and evolve core data models and metrics: Define and maintain shared models for product usage, customers, accounts, and key business metrics that support analytics, in-product customer insights, and AI/ML model training
• Build and operate foundational data products: Stay hands-on building models using SQL, Python, and Spark in a modern lakehouse environment (e.g., Databricks), with strong attention to data quality, availability, performance, and cost
• Define shared semantics: Design and maintain shared metric definitions and semantic layers so data is interpreted consistently across teams and systems
• Partner across teams: Work closely with Product to define foundational product concepts, with Data Platform on architecture and reliability, and with Business Analytics, Data Science, ML, and in-product insights teams as key consumers
• Set technical direction: Make pragmatic decisions about modeling standards, architecture, orchestration (Airflow/Astronomer), and tooling that balance near-term delivery with long-term maintainability