2024.01–2026.06
Cloud Data Engineer
Wiwynn / Wistron ITS
Data platforms, analytics, automation, and cloud delivery with Python, SQL, Azure Databricks, and ADF.
Wade Huang / Milespapa
Build ideas into working systems.
Milespapa is my personal technology brand — the place where I turn ideas into working systems through research, engineering, and AI-native development.
I am Wade Huang. Milespapa connects how I think, how I build, and the projects I choose to make real: from defining the problem and designing the architecture to implementation, testing, CI/CD, deployment, and a result people can actually use or inspect.
About Milespapa
A personal technology brand built around making things real.
Milespapa is not a company façade or a collection of disconnected side projects. It is the public home for my engineering practice — a place to explore different domains while keeping the same standard: understand the problem, make the reasoning visible, build the system, and verify the outcome.
The projects may span data engineering, AI, automation, backend systems, cloud and platform engineering, or quantitative systems. What connects them is the way they are built, not a single technology or domain.
- Wade Huang
Research mindset and engineering experience.
- Milespapa
The brand that turns that way of working into a visible practice.
- Working systems
Architecture, implementation, validation, and delivery.
- Projects
Concrete outcomes across different engineering domains.
Experience Selected career timeline
2022.06–2024.01
Cloud Engineer / Cloud Team Leader
EnSense
Cloud engineering, backend delivery, API development, and team-level technical coordination.
2017.02–2022.06
Research Assistant / Data Analyst
Academia Sinica
Research-driven data analysis, quantitative reasoning, reproducibility, and root-cause investigation.
2016.06–2016.12
Research Assistant
National Taiwan University
Research support and data analysis in an academic environment.
2015.01–2016.06
Associate Engineer
Industrial Technology Research Institute
Engineering work that formed the foundation of later data and system-oriented practice.
Publications Selected references
-
Chen, Y.-Y., Huang, W., Wang, W.-H., Juang, J.-Y., Hong, J.-S., Kato, T., & Luyssaert, S. (2019). Reconstructing Taiwan’s land cover changes between 1904 and 2015 from historical maps and satellite images. Scientific Reports, 9, 3643.
doi:10.1038/s41598-019-40063-1 -
Chen, Y.-Y., Huang, W., Cheng, C.-T., Hong, J.-S., Yeh, F.-L., & Luyssaert, S. (2022). Simulation of the Impact of Environmental Disturbances on Forest Biomass in Taiwan. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006519.
doi:10.1029/2021JG006519 -
Chen, Y.-Y., Zeng, Z.-L., Huang, W., & Lin, S.-Y. (2026). From global models to local indicators: Assessing land-use transitions and conflicts. Land Use Policy, 164, 107937.
doi:10.1016/j.landusepol.2026.107937
Capability stack
Built layer by layer, not as isolated skills.
Research discipline shaped the analysis layer. Data engineering added reproducibility and scale. Backend and cloud work added system boundaries and operations. AI-native development now accelerates the full delivery loop.
01
Data engineering
Python, SQL, Spark, Databricks, pipelines, data quality, analytics models, and BI delivery.
02
Backend & systems
APIs, real-time communication, service boundaries, failure handling, and maintainable implementation.
03
Cloud & platform
AWS, Azure, Linux, containers, CI/CD flows, deployment, monitoring, and production troubleshooting.
04
AI-native delivery
Using AI for research, decomposition, coding, review, and iteration while keeping engineering acceptance explicit.
Engineering philosophy
Make the reasoning visible.
Research taught me to define the question before interpreting the result. Engineering added another requirement: the system must keep working after the explanation is over.
That is why I care about explicit constraints, ownership, failure behavior, acceptance criteria, observability, and documentation. Clean code matters, but clarity across the whole delivery path matters more.
AI-native development
AI accelerates the loop. Engineering still owns the result.
I use AI to accelerate investigation, decomposition, implementation, review, and iteration. Architecture, testing, debugging, security, deployment, and final acceptance remain deliberate engineering work.
Selected work
The projects show how the pieces come together.
Milespapa projects show architecture, tradeoffs, constraints, delivery flow, and working outcomes across data, cloud, backend, automation, AI, and quantitative systems.