Doni Putra Purbawa
Cloud Architect & Senior Backend Engineer | AWS, Fintech, Cloud & AI
I'm a hands-on Cloud Architect and backend engineering manager with 6+ years building production systems for a Japanese SaaS company. My expertise spans AWS infrastructure, fintech payment platforms, microservices architecture, and AI-powered application backends. I care about correctness, reliability, and shipping systems that teams can operate with confidence.
Engineering Philosophy
Good cloud architecture is quiet — your users never think about the networks, databases, deployments, and observability keeping their products available. That reliability is the goal.
I approach every system with three questions: Is it secure? Is it observable? Can someone else maintain it in six months? The answer to all three should be yes before anything goes to production.
I believe in simple solutions to complex problems, strong code review culture, incident postmortems without blame, and continuous investment in developer tooling and deployment confidence.
Technical Skills
Languages
Cloud Architecture & Infrastructure
Platform & Backend Architecture
Payment Integration
AI & Machine Learning
Tools & Practices
Certifications
AWS Certified Cloud Practitioner
Amazon Web Services
JLPT N4 (Japanese Language)
Japan Foundation
TOEIC 765 (English Proficiency)
ETS
Education
Master of Informatics Engineering
Institut Teknologi Sepuluh Nopember (ITS)
Surabaya, Indonesia
GPA 4.00 / 4.00
- Research focus: Deep learning for anomaly detection in medical IoT
- Published Elsevier Q1 journal paper on COVID-19 detection
- Co-authored book: Machine Learning & Deep Learning using Python
Bachelor of Informatics Engineering
Universitas Brawijaya
Malang, Indonesia
GPA 3.79 / 4.00
- Student exchange at Saga University, Japan (2016)
- Thesis: Web-based academic information system
Student Exchange Program
Saga University
Saga, Japan
- Immersed in Japanese engineering and academic culture
- Foundation for JLPT N4 language achievement
- Sparked interest in Japan technology sector and career opportunities
Publications & Books
Deep Learning-Based Anomaly Detection for i-Nose C-19 Electronic Nose System
2022Heliyon (Elsevier) — Q1 Journal
Classification of COVID-19 exhaled breath biomarkers using CNN-LSTM hybrid model trained on MOS sensor time-series data. Achieved 94% accuracy on held-out test set.
Machine Learning & Deep Learning using Python
2022Book — Co-author
Contributed chapters covering deep learning fundamentals, model training workflows, and practical deployment patterns for Indonesian engineering students.