• Perform advanced data analysis using statistical and analytical techniques • Develop, deploy, and interpret predictive and AI/ML models • Build and apply dynamic process models for industrial systems • Translate complex data insights into actionable recommendations • Collaborate with cross-functional teams and present findings effectively • Support on-site installation and commissioning activities as required

• Build and maintain robust data models using dbt, ensuring data accuracy and accessibility across the organization through automated processes and improved data quality checks • Design and migrate dashboards and reports to modern BI tools like Count, translating complex data requirements into clear visualizations that support merchant and issuer success. • Develop analytical frameworks for merchant segmentation, performance metrics, and campaign measurement, providing insights that drive strategic decisions and optimize partner relationships. • Partner with sales, account management, and product teams to understand data needs, scope technical solutions, and deliver analytics that directly support revenue growth and operational efficiency. • Lead pitch development and technical investigations, identifying opportunities to improve data workflows, reduce costs, and enhance the scalability of analytics infrastructure.

• Design and implement a formal analytics data layer using AWS Glue, Presto, and LookML • Collaborate within the Data Science & Analytics team and across Product & Engineering to define, document, and maintain alignment on metric definition and data lineage • Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports and dashboards • Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences • Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review

• 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.