• Design, build, and maintain robust, production-grade data models (e.g., in dbt) that power incentives, promotions, and lifecycle analytics, including standardized fact/dimension tables and a consistent metrics layer. • Partner with Data Engineering to model source data from multiple systems (e.g., marketing platforms, event streams, transactional data) and implement efficient, auditable ELT patterns in a modern cloud warehouse. • Define and operationalize KPI and metric definitions for marketing efficiency and ROI; enable self-serve analytics in BI tools by implementing clean, documented semantic models and LookML (or equivalent). • Set and enforce data quality standards with automated testing, lineage, documentation, and monitoring to ensure stakeholders can trust dashboards and analyses used to manage millions in annual spend. • Collaborate with Product, Marketing, and Engineering to scope requirements, prioritize a roadmap, and deliver high-impact datasets for experimentation, attribution, cohorting, and lifecycle performance reporting. • Continuously improve performance, reliability, and cost efficiency of pipelines and queries; drive best practices in version control, code review, and CI/CD for analytics engineering.