• Own revenue account reconciliations, accounting analyses, and data validation activities to ensure the accuracy and integrity of financial reporting. • Collect, analyze, and reconcile revenue data across accounting systems, ERP platforms, and operational databases, proactively identifying and resolving data discrepancies, anomalies, and reporting issues. • Support month-end, quarter-end, and year-end close processes, including revenue reporting, reconciliations, and preparation of audit-ready documentation for internal and external audits. • Develop and maintain SQL queries, Python-based analyses, reports, and data extracts to investigate revenue trends, improve reporting accuracy, and deliver actionable business insights. • Partner with Revenue Accounting, Finance, Business Operations, Product, and IT stakeholders to support revenue-related initiatives, new product launches, and order-to-cash processes. • Contribute to process improvements by documenting business rules, automating manual reporting activities, enhancing data workflows, and strengthening data quality controls.

• Supports data management, reporting, and performance monitoring activities for statewide rural health transformation initiatives. • Assists in development and maintenance of dashboards, reports, and structured data products used to track program implementation and provider performance. • Performs data extraction, cleaning, validation, and analysis to support recurring reporting requirements and ad hoc requests. • Maintains structured datasets and tracking systems aligned with program reporting and compliance needs. • Develops standard reports and visualizations to support performance monitoring across provider initiatives. • Assists in identifying trends, inconsistencies, and data quality issues. • Coordinates with data, technical assistance, and program teams to support data-driven decision-making. • Contributes to preparation of reports, presentations, and documentation for state and federal stakeholders. • Maintains organized, audit-ready documentation of data sources, methodologies, and reporting outputs. • Supports continuous improvement of data processes and workflows. • Up to 25% travel may be required.

• Work in the discovery phase of requests with requesters and business stakeholders. • Understand the objective of the request and which analytical products will be built later, such as dashboards, automations, reports, indicators/metrics, or analytical models. • Map indicators, metrics, business rules, and data requirements. • Create and document functional requirements related to data ingestion. • Support the refinement of requests with the teams involved. • Work in partnership with functional ingestion analysts, analytics engineering, and other stakeholders. • Ensure that requirements are clear, complete, and aligned with business needs. • Contribute to reducing rework and additional ingestion requests during or after analytical development. • Support the organization and prioritization of the information required to enable more accurate data deliveries.

• Shape the AI & Data Vision • Own the Predictive Roadmap: Drive the vision and strategy for our external AI/ML models, advanced analytics features, and data products. • Co-Innovate with Clients: Embed deeply with strategic customers and design partners to validate ideas, uncover raw user needs, and co-develop next-gen features. • Tell the Story of Data: Partner with UX and Data Science to turn complex backend algorithms into beautiful, intuitive data visualizations and high-impact product demos. • Execute & Ship with Impact • Translate Complexity: Act as the ultimate bridge. Translate messy customer business problems into precise technical specs, transformation logic, and data requirements for our engineers and data scientists. • Own the GTM: Collaborate with Product Marketing and Sales to launch features that aren't just technically impressive, but actually move the needle on SaaS metrics (Adoption, Retention, ARR expansion). • Hypothesize & Validate: Use your analytical skills to dive into data assets, validate assumptions, and prove (or disprove) product hypotheses before writing code.