• Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization • Empower stakeholders by translating business requirements into scalable data models, dashboards, and tools • Partner with engineering, data science, product, and business teams to ensure alignment on priorities and data solutions • Build frameworks, tools, and workflows that maximize efficiency for data users while maintaining high standards of data quality and performance • Use modern development and analytics tools to deliver value quickly, while ensuring long-term maintainability

• Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization. • Empower stakeholders by translating business requirements into scalable data models, dashboards, and tools. • Partner with engineering, data science, product, and business teams to ensure alignment on priorities and data solutions. • Build frameworks, tools, and workflows that maximize efficiency for data users, while maintaining high standards of data quality and performance. • Use modern development and analytics tools to deliver value quickly, while ensuring long-term maintainability. • Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery. • Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly). • Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.

• 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