🧠 Data Engineering • 🤖 Machine Learning • 🏗️ Software Architecture

I design and build data-intensive software systems that operate in real environments — messy data, real-time constraints, reliability requirements, and long-term maintainability.

🎓 Engineering Doctorate (EngD) in Data Science
JADS / Eindhoven University of Technology

My work sits at the intersection of data engineering, machine learning, and software architecture, with a focus on systems that remain understandable and maintainable long after the demo phase.

I help organizations design and implement reliable data platforms and intelligent systems.

• real-time data pipelines
• streaming architectures
• ETL and data integration
• scalable data platforms

• model deployment and monitoring
• time-series forecasting
• applied ML for operational systems

• distributed systems design
• cloud-based data platforms
• maintainable data system architectures

flowchart TD
A[Sensors / External Data] --> B[Data Ingestion]
B --> C[Data Processing]
C --> D[Analytical Storage]
D --> E[Machine Learning]
E --> F[Visualization / Decision Support]

A central project of my work is the design and development of an Urban Digital Twin platform for the City of ’s-Hertogenbosch.

The system integrates:

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

The platform is designed as a living system that evolves with real data rather than a static simulation model.

Sensors / External Data Sources
            │
            ▼
     Data Ingestion Layer
   (Streaming APIs / Kafka)
            │
            ▼
     Data Processing Layer
      (ETL / Stream Jobs)
            │
            ▼
    Analytical Storage Layer
   (Time-Series / Data Lake)
            │
            ▼
 Machine Learning & Forecasting
            │
            ▼
Visualization & Decision Support

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, focusing on data infrastructure, reproducibility, and clarity of implementation.

Through DataTwinLabs, I collaborate with public organizations and industry partners on:

• urban data platforms
• digital twin systems
• real-time analytics pipelines
• applied AI 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@outlook.com