Curaden, a Swiss-based oral healthcare company, partnered with Axon Active for healthcare data platform development across Switzerland and Vietnam. A dedicated development team of ten engineers delivers end-to-end data solutions on Google Cloud Platform, supporting Curaden's transition toward data-driven decision-making across sales, operations, and finance.
Curaden is a Swiss-based oral healthcare company delivering products and educational solutions to dental professionals and consumers worldwide. To modernise its data landscape, Curaden developed the Data Platform Ecosystem through healthcare data platform development—a cloud data platform designed to unify fragmented data sources and provide consistent, reliable insights across the business. The platform supports Curaden’s broader Swiss healthcare software ecosystem.
Structured across three layers—System Integration, Data Engineering, and Analytics & Reporting—the platform integrates data from internal systems and external platforms, automates data ingestion and processing on Google Cloud Platform, and enables business users to access insights through tools such as Power BI and Looker Studio, reflecting the data engineering services Axon Active provides through custom healthcare software development for Curaden.
Axon Active’s dedicated development team follows a Kanban-based Agile workflow with continuous delivery, daily standups, stakeholder reviews with Curaden, and retrospectives, ensuring transparency and continuous improvement without fixed sprint cycles.
A regular Vietnam–Switzerland overlap enables real-time collaboration, while asynchronous communication supported by clear documentation maintains momentum outside working hours.
The team ensures long-term continuity and ownership, coordinated by a Swiss-based account lead. Task management is handled through Asana and Jira, providing structured planning, tracking, and visibility across the project.
The team contributes across the full lifecycle of Curaden’s Data Platform, delivering healthcare data platform development from solution design and implementation to operation and continuous improvement. Work is structured around three core areas:
Together, these capabilities support a cloud data platform that remains maintainable, scalable, and reliable for both operational use and long-term analysis.
Although primarily focused on data, the team also builds supporting interfaces and tools to keep the platform practical and user-friendly, using NextJS on the frontend and NodeJS, JavaScript, or Java on the backend, with tight alignment to ensure accurate reflection of system capabilities and performance; the team also delivers HubSpot and Shopify projects.
Data storage and orchestration form the backbone of the platform. BigQuery powers the platform’s BigQuery analytics as the primary data warehouse, while Apache Airflow and Spark manage batch and streaming data pipelines. PostgreSQL and MariaDB support targeted transactional use cases. The overall architecture is designed to ensure data integrity, efficient querying, and long-term scalability.
Data comes from multiple systems and formats, including regional ERPs, APIs such as Amazon SP-API, and Excel files. The engineering team builds and maintains data ingestion pipelines that normalize incoming data into a consistent structure within the platform. This enables downstream analytics and reporting to operate on reliable, comparable datasets.
Pipelines are orchestrated using tools like Airflow, with monitoring and logging in place to detect failures or delays. When issues occur, they are investigated and resolved as part of the team’s regular workflow. Maintenance and incremental improvements are ongoing to keep pipelines stable as data sources evolve.
The platform separates data processing from data consumption. While engineers manage pipelines and infrastructure, business users access curated datasets through dashboards and reporting tools like Power BI and Looker Studio. This allows non-technical users to work with data without needing to understand the underlying systems.
Data quality is addressed through validation rules, transformation checks, and monitoring at different stages of the pipeline. The team reviews data inconsistencies and refines ingestion logic when needed. Over time, this helps improve trust in the data used for reporting and decision-making.
New requirements are handled through a Kanban-based workflow with daily coordination meetings. The development team works closely with the Product Owner and stakeholders to continuously refine and adapt data models, pipelines, and dashboards based on evolving business needs, ensuring smooth incremental delivery without disrupting existing systems.
