• 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.

• Conduct descriptive and exploratory analyses to support product, growth, and business decisions. • Build and maintain dashboards and reports to monitor KPIs, experiments, feature launches, and overall performance. • Partner with Product and Growth teams to analyze user behavior, funnels, and retention. • Design and maintain clean, well-documented data models in Snowflake and dbt. • Improve metric definitions and ensure consistency across teams and dashboards. • Contribute to the reliability and scalability of our analytics infrastructure. • Support experimentation efforts (A/B testing) by helping define metrics, analyze results, and interpret outcomes. • Help teams frame the right questions and turn data into clear recommendations. • Communicate insights clearly to non-technical stakeholders. • Contribute to a strong data-driven culture through documentation, presentations, and workshops. • Act as a thought partner for teams making strategic or operational decisions.

• Collect and understand business requirements and translate those requirements into an actionable data warehouse plan. • Knowledge of multi-dimensional and tabular design patterns and ability to identify solutions. • Work within the SDLC framework in multiple environments. • Define and implement best practices across database design and ETL. • Direct ETL development, demonstrating an understanding of key concepts of ETL/ELT.