• Network Reporting & Regulatory Compliance • Interchange Analysis & Revenue Attribution • Strategic Modeling & Network Economics • Data Engineering & Upstream Influence

• Design, build, and maintain scalable data pipelines that extract, transform, and load (ETL/ELT) data from various internal and external systems • Develop efficient and reliable data models that support reporting and analytics needs across business functions • Implement data quality checks and monitoring to ensure accuracy, completeness, and consistency of critical datasets • Manage and optimize data storage solutions (e.g. data warehouses, data lakes), ensuring scalability, security, and performance • Partner closely with analysts and business stakeholders to understand their data needs and deliver well-documented, high-quality data assets • Work with Engineering teams to instrument new data sources and drive data-driven culture across the organization • Develop automated processes for data ingestion, transformation, and validation; implement monitoring to proactively detect issues • Provide insights via easily digestible visualizations in Tableau and ad-hoc

• We are looking for a Lead Analytics Engineer to join our data team. • Reporting to the Hiring Manager, you will be responsible for applying data engineering best practices to analytics code to transform, test, and document data. • You will provide clean and organized data sets to end users. • You will be provide expert code reviews and mentorship to other engineers in the data space • You will be responsible for building and maintaining composable data models, as well as optimizing SQL query performance for the models you build. • You will transform raw data into business insights, working closely with stakeholders and developing analyses to answer critical business questions. • You will create data visualizations and help stakeholders explore and understand the data visualization tools available to them.

• 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.