Responsibilities
- Lead the development and ongoing improvement of core data structures and performance indicators, ensuring alignment across analytics, customer-facing features, and machine learning systems
- Develop and maintain essential data assets using SQL, Python, and Spark within a lakehouse architecture, prioritizing accuracy, efficiency, accessibility, and cost-effectiveness
- Establish consistent definitions for key metrics and create reusable semantic layers to ensure uniform data interpretation enterprise-wide
- Collaborate with Product, Data Platform, Business Analytics, Data Science, ML, and product insights teams to align on data requirements, system design, and integration needs
- Guide technical strategy by making balanced decisions on modeling practices, data architecture, workflow orchestration with Airflow or Astronomer, and tool selection to support both immediate and long-term goals
