• Lead end-to-end delivery of finance data models that support reconciliations, journal entry preparation, and close workflows, with a focus on reliability, controls, and audit readiness. • Own the Financial Reporting semantic layer strategy and execution, including definition governance and adoption across reporting surfaces. • Drive migration and modernization of reconciliation and close-support reporting into standardized, automated outputs where appropriate. • Partner with Accounting and Finance stakeholders to translate close and audit requirements into durable data contracts, model specifications, and delivery roadmaps. • Build and evolve reporting data products that make financial insights easy to consume and hard to misinterpret, partnering with BI and Financial Reporting tools (for example Sigma and Workiva). • Build the foundations for AI-assisted finance analytics by enabling AI agents to safely access governed finance datasets (for example, through well-defined metrics, strong documentation, and permissioned datasets) to support self-service questions and reporting workflows. • Establish strong analytics engineering practices across the stack (testing, documentation and glossary stewardship, monitoring/alerting, code review, release discipline, and operational ownership).

• Transform raw blockchain and production data into clean, reliable datasets • Own core analytics models across our medallion architecture • Partner with data engineering on ingestion pipelines • Model transaction data across dozens of chains (swaps, bridges, fees, capital flows) • Integrate pricing feeds and partner data for competitive analysis • Gather requirements from product, business, and operations teams • Design and standardize key metrics • Build dashboards and enable self-service analytics

• Core Team priorities for 2026: • Analytics data warehouse v2: Design, implement, and migrate to a new analytics warehouse, owning modeling patterns, layer contracts (Silver, Gold, Semantic), and metric definitions. • Clear layer ownership: Define interfaces and responsibilities across ingestion, transformation, and analytics to improve velocity and trust. • Clear role definitions: Within the Core team, identify how the Data Analysts and Analytics Engineers work together on the team’s goals. • Embedded analytics: Launch Omni for client-facing analytics, establish best practices, and train Product and Business Analysts. • Production analytics tooling: Specify and integrate tools for data quality, anomaly detection, and monitoring. • Team growth: Scale the Core Analytics team from 4 to 8. • Platform scale: Support analytics infrastructure for 10 products (9 net new).

• Deliver data warehouse and analytic solutions • Write ETL packages, develop visualizations, perform data and statistical analyses, and administer systems to deliver information to the health system • Work with business users to develop analytics solutions • Develop, design and support applications and relational databases • Assist with development of pipelines to synthesize raw data into actionable information • Evaluate data quality and interpret results in a clear, concise manner • Develop documentation on requirements, decisions, design, modifications, and any associated maintenance • Support analytics projects and collaborate with the business to gather and execute requirements • Work collaboratively with and support multi-departments efforts and projects • Participate and contribute to overall training development, maintenance, and facilitate trainings both internally and externally as needed • Leverage SQL, SSMS, and Databricks to conduct healthcare analytics and generate comprehensive reports for data-driven decision making • Engineer and implement structured programming solutions to automate database and server connections, transforming complex data into accessible, user-friendly content while eliminating manual processes