• Complete onboarding in Notion and meet your teammates through Get-To-Know-You calls. Get oriented in our core tools: dbt, Github, Redshift, Omni, Slack, and Linear. Start reading existing documentation on our Reporting Hub architecture and canonical metric definitions. • Audit active Reporting Hub models across Finance, Marketing, Sales, Product Strategy, and Creator Lifecycle. Map current churn logic implementation and identify inconsistencies in canonical metric definitions. Assess Redshift performance strain areas and flag fragile model dependencies. Publish a written architectural assessment memo with your initial findings and recommended priorities. Serve as the data team’s voice in cross-functional discussions about metric definitions, attribution logic, and analytical rigor across Finance, Product, and Marketing. • Propose and begin executing a 6–12 month modernization roadmap for our modeling layer. Refactor the highest-risk models across all business verticals. Clarify attribution model contracts and improve cross-functional documentation standards. Measurably reduce warehouse inefficiencies through distribution and sort key optimization, not infrastructure scaling. Improve Segment event modeling structure and align event design with reporting needs in partnership with Product and Engineering. Drive company-wide adoption of canonical metrics by working directly with functional leads to replace ad hoc definitions with documented, auditable standards. • Complete the Reporting Hub foundation across business verticals with canonical metric consistency enforced. Enable true stakeholder self-serve, so teams can explore data without relying on the Data team for routine questions. Reduce model rework caused by upstream ambiguity through upstream design patterns and documentation. Raise the overall modeling sophistication of the Data team through mentorship, standards, and shared tooling. Leadership makes resource allocation and growth decisions using a shared metric layer they trust without qualification. Finance closes faster because revenue and subscription models are reliable and consistent. Product and the broader company make decisions with cohort and performance data they no longer need to manually verify, allowing the data team to ship faster with less rework.