Freelancer profile translated to English.

Description

I am an enthusiastic freelance data specialist with 10 years of experience in developing scalable, high-quality data solutions in diverse environments. Thanks to my background in mathematics, I am adept at simplifying complex business issues and translating them into practical, usable solutions. Colleagues appreciate my no-nonsense approach: critical when necessary, but always focused on achieving results.

I have a passion for data and AI projects, especially for building modern data platforms such as Databricks, Azure Synapse, and Microsoft Fabric. I work according to best practices in software development, CI/CD, and data quality. I ensure reliable data through good validation, testing, and monitoring, and build solutions that are scalable and maintainable.

In addition to technical knowledge, my experience as a consultant brings strong communication skills to advise at a strategic level, develop data solutions that align well with the information needs of stakeholders, and transfer knowledge effectively.

Languages

Dutch
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Utrecht (up to 50km)

Waterschap De Dommel,
Data Platform Engineer
October 2025 - July 2026 (9 months)
I work on the implementation of a data platform. I focus on collecting, managing, and delivering high-quality data products and analytical solutions throughout the organization.
• I shape the technical architecture of the data platform, including Microsoft Fabric and PostgreSQL/PostGIS for processing geospatial data.
• I design, build, and maintain geodatabases to enable standardized collection, management, and distribution of geodata within the entire organization (PostgreSQL, PostGIS, Python).
De Nederlandsche Bank,
Lead Data Engineer
December 2022 - September 2025 (2 years and 10 months)
Amsterdam, Netherlands
As lead developer, I work on the development of a data platform for the Statistics department. I am involved in the technical architecture and am a key point of contact for Python software architecture.
• Defining and developing a scalable statistical platform (Databricks, MS Fabric, and Microsoft Azure) used by 12 teams, implementing a Lakehouse architecture and reusable, platform-independent Python code.
• Designing and implementing robust PySpark pipelines to automate statistical reporting, improve data quality, and reduce manual workload using, among others, Azure SQL, Medallion architecture, Change Data Capture (CDC), and Slowly Changing Dimensions (SCD2).
• Developing production-quality Python packages and Dataiku plugins to support statistical analysis for business teams.
• Optimizing MS Fabric pipelines and Power BI reports by implementing dimensional modeling and other best practices.
• Improving the performance of key statistical functions from hours to seconds by optimizing Python-based algorithms.
• Implementing DevOps best practices (Azure DevOps, SonarQube), logging (Azure Log Analytics / Application Insights), and deployment tools (Nexus Repository Manager, Databricks Asset Bundles, Azure Pipelines).
• Shaping platform orchestration using Azure Service Bus, Azure Functions, and the Databricks API to deliver efficient, user-friendly workflows.
Royal HaskoningDHV,
Data Scientist / Data Engineer
August 2020 - August 2022 (2 years and 1 month)
Utrecht, Netherlands
As a consultant in data science and data engineering, I help (governmental) organizations explore, design, and implement data solutions.
• Development and execution of a data science roadmap, implementation of a scalable data quality framework, and prioritization of high-impact data science use cases for various water authorities; implementation of water quality dashboards (Python, Power BI).
• Analysis of data quality of water authority sensor data (Historian) using data science techniques (Python).
• Design and implement object detection models (computer vision) to automatically recognize environmental and health risks in aerial photographs, enabling proactive risk assessment (PyTorch, Databricks, MLflow).
• Application of remote sensing and machine learning on satellite imagery to optimize water management and identify ecological risks (Azure Synapse; Python: rasterio, GDAL, GeoPandas, scikit-learn).
• Building data science expertise within water authorities by setting up and guiding a two-year DS/DE internship program. Mentored over 20 junior professionals through workshops and project coaching; conducted workshops on data science, data engineering, and Python development.
• Development of real-time, ML-driven flood risk prediction models for rivers (MLflow, Azure Machine Learning).
• Implementation of ETL pipelines and reporting dashboards (Databricks, PySpark, Power BI) to provide near-real-time insights and accelerate decision-making for Dutch water authorities.

Clients: Waterschap De Dommel, Hoogheemraadschap van Delfland, Waterschap Limburg, Het Waterschapshuis, DCMR Environmental Protection Agency Rijnmond.