• Champion a “data as a product” mindset: Set the standard for robust documentation, testing, and data model design – making revenue data products easy to understand, trust, and extend. • Mentor and uplevel the team: Share best practices in analytics engineering, code quality, and documentation; coach others to adopt product-minded approaches and raise AE standards across the org. • Partner cross-functionally with Finance, Data Science, Data Engineering, and Revenue stakeholders to define the future vision for key revenue data products, surfacing opportunities for innovation and improved business impact. • Continuously improve business-critical analytics workflows for revenue that power Investor Relations reporting and all downstream revenue consumers, ensuring reliability, scalability, and transparency. • Drive root-cause analysis and ask “why” to deeply understand the drivers of revenue data issues, proactively identifying improvements to workflows, code, and models. • Implement and advocate for orchestration best practices to ensure the reliability and timeliness of revenue data pipelines. • Leverage AI tools (e.g., Claude Code, Cursor, MCP servers) to boost developer productivity and workflow efficiency, and share learnings with the team. • Contribute to the adoption of modern AE standards (e.g., WAP—Write Audit Publish) and help define best practices for documentation, testing, and code review.