🧠 Data Engineering • 🤖 Machine Learning • 🏗️ Software Architecture

I build data-intensive software systems at the intersection of data engineering, machine learning, and software architecture. My work is production-oriented: messy data, latency, scale, reliability, and systems that remain understandable long after the demo stops impressing people.

• 🎓 Background: Engineering Doctorate (EngD) in Data Science
• 🔬 Core interests: streaming platforms, digital twins, spatiotemporal systems, applied AI
• 🐍 Primary ecosystem: Python (open, inspectable, production-first)

My work is implementation-driven and focused on operational relevance. A central project is the design and development of an Urban Digital Twin for the City of ’s-Hertogenbosch, integrating:

• real-time streaming pipelines
• geospatial and spatiotemporal data processing
• time-series storage and analytics
• forecasting and machine-learning models
• interactive 3D visualization for exploration and decision support

The system is designed as a living platform rather than a static model. This work has been presented in academic and professional venues and continues to evolve toward real-world deployment.

My research sits at the boundary between systems engineering and data science:

• real-time and streaming analytics
• data integration and interoperability
• federated and distributed data processing
• ML deployment, evaluation, and monitoring in production
• reliability, scalability, and reproducibility of data-driven systems

Architectural choices often dominate model performance. That is where I spend my time.

I prefer systems that are:

• composable — replaceable parts, minimal lock-in
• explicit — clear interfaces and data contracts
• inspectable — debuggable without folklore
• maintainable — designed for the second year, not the second week

Complexity should be visible, not hidden.

I am an open-source practitioner, primarily within the Python ecosystem. I value clarity over cleverness and reproducibility over novelty. If a system cannot be reasoned about, it does not scale.

Through DataTwinLabs, I collaborate with public organizations and industry partners on data platforms, digital twins, and applied AI systems.

I am open to collaboration on research, engineering, and applied projects where data meets real-world systems.

• 🌐 Website: https://datatwinlabs.nl
• 💼 LinkedIn: https://www.linkedin.com/in/danielwondyifraw/
• 📄 Publications / talks: https://www.jads.nl/news/paving-the-way-for-sustainable-urban-construction/
• 📫 Contact: datatengineerd[at]outlook[dot]com