Wim Roten

Expert data engineer who combines data-platform architecture with a hands-on implementation role. 7+ years on Microsoft Azure and Databricks (Apache Spark, PySpark, Python, SQL): designed, built and maintained an organisation-wide data platform and lakehouse end-to-end, from architecture and cost estimation to delivery, documentation and reliable operation. Supports reporting, analytics and data science with high-quality, scalable solutions, and advises stakeholders on data, reporting and architecture.

Data Architect at EIT Food (2022-07 – 2026-05)

• Acted as the sole technical reference for the organisation's data platform: set the data engineering and modelling standards, made the architectural decisions and owned quality and reliability end-to-end.
• Designed, built and ran the platform on Azure Databricks: Medallion architecture with Delta Lake and Unity Catalog, implementing ingestion and transformation in PySpark, Python and SQL with reusable, reviewed pipeline patterns.
• Built and optimised Azure Data Factory pipelines for orchestration, incremental loads and integration with source systems (Salesforce, NetSuite, SQL Server), including dependency management and monitoring.
• Translated business logic into robust dimensional models (Kimball star schemas, DWH concepts) and built the organisation's Power BI reporting (15+ reports) on governed semantic models with row-level security.
• Guided business users and peers: coaching, documentation and knowledge sharing, raising the maturity of data practices.
• Led the migration from a legacy.NET + SQL Server system to Salesforce (7 years of historical data) without interrupting reporting continuity.

Analytics Consultant at Lytix (2020-03 – 2022-06)

• Managed delivery as project manager: scoped the project, assigned workloads, coordinated team sprints and handled stakeholder updates.
• Developed a computer vision prototype to classify shoe characteristics using Python, scikit-learn and OpenCV, achieving over 90% accuracy.
• Created ETL pipelines with Azure Data Factory and Databricks, integrating a wide range of sources (SQL Server, Oracle, SAP, Google Analytics, SFTP file feeds) to centralise and process public transport datasets.
• Developed a predictive maintenance proof-of-concept for tram vehicles using sensor data and ML models.
• Delivered Power BI training to 7–8 users monthly and supported the organisation as Power BI environment admin.
• Led cloud migration from SSIS/SSAS/SQL Server to the Azure Data Platform, improving scalability and performance.
• Developed an RShiny application for the Municipality of Zoetermeer to visualise predicted road quality.
• Applied supervised ML techniques in R to detect fraud patterns in Kenyan customs data, contributing to risk profiling methods.

Data Scientist at Atos (2019-07 – 2020-02)

Bachelor in Mathematics – KU Leuven (2012 – 2018)