• Support financial planning, forecasting, and budgeting for assigned business units, with a focus on revenue, gross margin, and operating cost structures. • Build and maintain financial models that support strategic planning, scenario analysis, and organizational decision-making. • Analyze performance trends, identify drivers of financial results, and communicate insights to operational and executive stakeholders. • Prepare monthly and quarterly reporting packages for business partners and leadership. • Serve as a primary finance partner to Operations and People Operations/HR, supporting compensation modeling, staffing plans, and organizational design discussions. • Support strategic initiatives, market evaluations, and business case development; present findings clearly to cross-functional and senior leaders. • Support the design, build, and maintenance of dashboards and reporting tools that give leadership real-time visibility into financial and operational performance. • Monitor key operational KPIs, translate them into financial impact, and proactively flag gaps, risks, or trends across markets and departments. • Translate ad hoc and recurring reporting needs into clear technical requirements for Data Engineering, acting as the bridge between business stakeholders and the data team. • Lead the adoption of modern analytics and reporting tools (e.g., BI platforms, automation, AI-assisted reporting) to make insights faster and more accessible. • Establish and maintain data integrity, consistent definitions, and single-source-of-truth reporting across finance and operational data sets. • Partner with data/IT teams as needed to improve data pipelines, structure, and self-service reporting capabilities. • Independently write and optimize SQL queries against our BigQuery data warehouse to support ad hoc reporting requests from operational and executive stakeholders, with rigor and a fast turnaround.

• Bring structure to complex data and shape the future of digital rehabilitation • Act as the central bridge between business, product, and tech teams • Translate ambiguous business, product, or operational requests into clear metrics, decision-ready problems, and actionable recommendations • Design reusable domain models, semantic logic, data sources, and KPI definitions from scratch • Build focused, reliable data visualizations in Tableau to support business-critical processes and decisions • Design and pilot AI-supported workflows (using tools like Claude, ChatGPT, Cursor, or n8n) to streamline analysis, coding, and QA processes • Challenge assumptions constructively while acting as a project-managing bridge between product, sales, and operations teams • Resolve complex analytical data, source-system, and business-logic issues at their root cause within a regulated health-tech setup

• Design and oversee the creation of interactive dashboards, reports, and data storytelling tools that provide real-time insights. • Standardise reporting and metrics, ensuring we are focusing on right measures for the right audience • Establish and enforce data visualisation best practices, ensuring clarity, accuracy, and user engagement. • Modernise data presentation to executives, the board, and other stakeholders, utilising interactive, real-time, and high-impact dashboards that enhance decision making and operational efficiency. • Partner with product and engineering teams to integrate visualisation into SaaS products, enhancing customer experience. • Develop a data storytelling framework that enhances user understanding and engagement with analytics. • Work closely with marketing, product, and analytics teams to translate complex insights into digestible, visually compelling stories. • Create high-impact data presentations, reports, and interactive content for internal and external stakeholders. • Establish best practices for data communication and train teams on effective storytelling techniques. • Champion a data-driven culture, ensuring teams have the tools to interpret and communicate insights effectively

• Define and evolve governance practices by structuring governance frameworks and models; • Ensure consistency and quality of enterprise data by implementing controls and corporate standards; • Lead implementation of data catalog and lineage, driving cataloging and lineage initiatives; • Define data policies and standards, establishing policies and ensuring their enforcement; • Act as a reference for business areas, serving as an internal data consultant; • Monitor governance maturity by defining governance KPIs and metrics; • Solve complex data issues by investigating and resolving critical inconsistencies; • Promote a data-driven culture by conducting training and cultural initiatives.