Hi, I'm
Ching-An Wu
Data Engineering Consultant
I help businesses build reliable, scalable data infrastructure — from cloud pipelines to data lakehouses — so your team can make decisions backed by real data.
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About Me
I'm a data engineer who thrives at the intersection of infrastructure, data, and machine learning. My path started with a Master's in Computer Science (Robotics) at KTH Royal Institute of Technology in Stockholm, where I researched deep reinforcement learning on robotic systems.
Since then I've worked across the full data stack — building ML pipelines at PwC's Digital Factory, integrating streaming data at COCUS AG, and now leading data platform engineering at Diconium (Volkswagen Group) in Stuttgart.
I am available for freelance consulting with businesses in Taiwan, helping teams adopt modern cloud data architectures and build infrastructure that genuinely serves the people who depend on it.
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Services
Remote consulting and implementation for businesses in Taiwan looking to modernise their data infrastructure.
Data Pipeline Engineering
End-to-end design and implementation of scalable ETL/ELT pipelines using AWS Glue, PySpark, and Apache Airflow. From prototype to production-ready.
Cloud Data Architecture
AWS data platform consulting and implementation — Lake Formation, S3, Redshift, Step Functions. Architected for scale, cost-efficiency, and governance.
Data Lakehouse Migration
Migrate to a modern lakehouse architecture with Apache Iceberg. Incremental ingestion, schema evolution, and up to 80% faster pipeline performance.
MLOps & Data Mesh
Deploy production ML systems and build self-service data platforms following data mesh principles, enabling decentralised data ownership.
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Where I've Worked
Advanced Specialist Data Engineer @ Volkswagen Group(Diconium GmbH)
Jan 2025 – Present · Stuttgart, Germany
Specialist Data Engineer @ Volkswagen Group(Diconium GmbH)
Mar 2023 – Dec 2024 · Stuttgart, Germany
- ▸Developing APIs for the Cariad Data Access and Management Layer in support of the EU Data Act
- ▸Led AWS data platform implementation using Glue (PySpark), S3, Lambda, and Lake Formation
- ▸Retained as the sole data engineer by a global dental client, sustaining an NPS score of 10/10
- ▸Redesigned AWS Step Function pipelines, reducing runtime by 80%
- ▸Built CI/CD pipelines with Terraform to provision the full tech stack including security configs
- ▸Led data lake → lakehouse migration with Apache Iceberg for efficient incremental ingestion
- ▸Accelerated delivery by 25% through data mesh adoption and self-service CI/CD tooling
- ▸Collaborated on a recommendation engine improving e-commerce conversion rates
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Some Things I've Built
Data Lakehouse Architecture Migration
Led the architectural migration from a data lake to a lakehouse at a global client, enabling efficient incremental data ingestion from source databases. Reduced pipeline runtime by 80% by redesigning AWS Step Function orchestration and adopting Apache Iceberg.
EU Data Act Compliance API Layer
Engineered a data access API layer using Azure Functions to connect 60+ platforms and 200+ services across Volkswagen Group brands, enabling EU Data Act compliance through user data access rights, data portability, and third-party data sharing with ~200ms async response times.
Production Kubeflow MLOps Platform
Helped a Fortune 500 company develop, deploy, enhance, and maintain a production Kubeflow machine learning platform, and contributed implementation improvements to the Kubeflow manifests repository.
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