• Performing deep-dive analysis to understand and optimize key levers of our product’s marketing funnel: acquisition, activation, engagement, and retention. • Designing core metrics that serve as North Stars for team efforts and OKRs. • Conducting quarterly prioritized analysis projects targeted at revenue and user growth. • Conducting ad-hoc analyses as defined by cross-functional partners and prioritized weekly. • Managing and maintaining dashboards in Periscope that relate operational performance across company departments and track key company metrics. • Working with product managers and engineers to design data products and run A/B experiments. • Working directly with product and business stakeholders to translate the questions and hypotheses into structured analysis. • Owning data analysis and visualization, and communicating the outcomes/insights to various partners (marketing, data, product, eng) to aid strategic decisions for improvements in experience for users.

• Design and maintain internal QuickSight dashboards that allow leadership to track company performance in real-time. • Monitor and report on core commercial metrics including MRR/ARR, Net Revenue Retention (NRR), Churn, and CAC/LTV ratios. • Write high-performance SQL in Amazon Athena to join disparate datasets from S3 into clean, analysis-ready tables. • Act as a bridge between technical data and business strategy. You don't just report numbers; you highlight trends, risks, and growth opportunities. • Work with AWS Glue to ensure that internal stakeholders are always looking at "one version of the truth." • Identify drivers within data subsets to build "what-if" scenarios and predictive models for core SaaS metrics (MRR, NRR, LTV), enabling leadership to pinpoint root causes and accurately forecast growth.

• Partner with Data Science to develop and enhance predictive LTV models, contributing to feature design, cohort logic, retention modeling, revenue forecasting, and profitability estimation. • Act as the cross-functional authority on customer value, aligning Sportsbook, Casino, Marketing, Finance, Product, and Operations on interpretation and usage of LTV metrics. • Lead LTV-based acquisition performance measurement, shifting focus from short-term KPIs to long-term value creation (LTV/CAC, payback periods, cohort quality). • Translate model outputs into business narratives, enabling executives and senior stakeholders to understand drivers of value, forecast changes, and strategic trade-offs. • Standardize LTV reporting across the organization, ensuring consistent definitions, transparency of assumptions, and alignment between Analytics, Finance, and Marketing. • Embed LTV into strategic planning and capital allocation decisions, partnering with Marketing and Finance stakeholders on forecasting, scenario modeling, and long-range planning. • Drive scalable executive reporting, developing dashboards and materials that clearly communicate cohort trends, forecast updates, risks, and opportunities. • Strengthen the data foundation supporting LTV, partnering with Data Engineering to improve data pipelines, documentation, and quality controls.

• Trace issues back through data pipelines or code to uncover why something isn’t behaving as expected. • Write clear, concise Jira tickets explaining what was found, how it was fixed, and how to prevent similar issues. • Partner with engineers and product managers to make data and code more reliable, faster, and easier to understand. • Help build a culture of technical curiosity — where asking why and testing assumptions is part of every release. • Leverage AI tools to think, build, and ship faster.