• 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.

• Define and evolve analytics modeling standards, architectural patterns, and semantic layers across domains • Drive data mesh enablement across analytics • Own data governance for analytics, including data privacy • Design and maintain data contracts, SLAs, and SLOs for analytics datasets • Own and continuously improve CI/CD, developer experience, and observability for analytics • Optimise analytics performance and cost across transformation and BI layers • Act as the technical escalation point and mentor for Analytics Engineers

• Design, build, and maintain dbt models that transform raw event- and entity-level data into curated, analytics-ready datasets. • Develop, document, and test semantic and reporting layers in Looker (LookML). • Partner with Analysts and cross-functional stakeholders to ensure consistent metric definitions and accurate data pipelines. • Contribute to data modeling best practices, including naming conventions, incremental strategies, and schema evolution. • Implement data quality tests and validation checks to ensure reliability of production datasets. • Monitor and troubleshoot data issues, coordinating fixes with Data Infrastructure and Engineering.

• Design and execute scalable data models and transformation patterns across core data marts that power analytics and AI use cases across the business • Partner cross-functionally to translate business problems into data solutions that support AI-led analytics and intelligent decision making • Define and drive data governance and certification standards that establish trusted, enterprise ready data sets • Define and implement SDLC best practices for data transformation, including documentation, testing and version control to ensure reliability over time • Socialise best practice guides that enable spoke analytics teams to move faster with consistent patterns • Assess existing models and pipelines to identify opportunities to simplify, standardise and improve • Identify and propose new ideas that advance an AI-led analytics experience and improve how insights are generated