Freelance Data Engineer (Max. 3 days per week) – Ghent / Hybrid

Are you an experienced Data Engineer with a passion for Azure Databricks and cloud-based data platforms? Are you looking for a challenging freelance assignment (maximum 3 days per week, for an initial engagement of 3 months with the possibility of extension) where you take technical ownership of the evolution of our new data platform? Join us and help build the foundation for analytics and data-driven decision-making within IVC Evidensia Belgium!

Who are we?

IVC Evidensia is the largest group of veterinary practices, centers, and clinics in Europe, with over 2,500 locations across 20 countries and more than 42,000 employees. In Belgium, we currently have over 60 practices, centers, clinics, and pet crematoria spread across Flanders and Wallonia, supported by over 570 driven colleagues. We combine the passion and autonomy of local entrepreneurship on the clinic floor with the professional support and operational strength of our central support office in Ghent.

We are currently transforming the way data is managed and leveraged across our organization. As a Freelance Data Engineer, you will take technical ownership of the implementation and evolution of our Databricks platform within this journey.

What will you do?

You will work closely with our Data Analyst, business stakeholders, and Group Data teams to enable high-quality analytics. Your focus will be on the following pillars:

• Data Pipelines & Architecture: Design, develop and maintain scalable ELT/ETL pipelines in Azure Databricks. Work within the existing Bronze, Silver and Gold medallion architecture, ensuring new pipelines, transformations and datasets follow established patterns and best practices. Contribute to the continuous improvement of the architecture where appropriate.

• Data Integration & Platform Evolution: Support the continued transition from legacy reporting infrastructure to the Databricks Lakehouse platform. Onboard and integrate new data sources, including internal systems, APIs and external datasets, while ensuring consistency with the existing platform design.

• Performance, Reliability & Optimisation: Improve pipeline reliability, monitoring and observability. Identify and implement opportunities to optimise data processing, improve performance and manage platform running costs effectively.

• Data Quality & Engineering Standards: Implement appropriate validation rules, data quality checks and monitoring. Maintain clear documentation of data flows and solutions, and follow established engineering and DevOps/GitHub practices.

• Analytics Enablement: Develop and maintain reliable, well-modelled datasets that are ready for downstream consumption through Power BI, Databricks AI/BI capabilities and self-service analytics.