Available for projects, Q3 / 2026
Data systems
that earn
trust.
I'm Nam Vu Hai, a data engineer & backend developer with 4+ years building pipelines, APIs and observability platforms across Azure and AWS.
[ About ] - 02 / Index
Make the data
behave.
[ Who's behind the work ]
Hi, I'm Nam.
- Melbourne, Australia
- Master of Data Science, RMIT (ongoing)
BSc Software Engineering, RMIT VN - First Class Honour - Top 1 - RMIT Kaggle Competitions (Life Expectancy & Book Rating)
I'm a data engineer and backend developer with 4+ years of experience designing systems that move, store and surface data at scale. I care about the quiet parts: schemas, SLAs, retries, cost.
Most recently I led the data ingestion architecture for UDE Central Monitoring at NTT DATA VDS, working peer-to-peer with the Wolfsburg team in Germany. Before that, I built backend services in Quarkus, a customer data platform on HBase + Spark, and processed 10TB of daily logsfor some of Vietnam's largest news properties.
I'm now in Melbourne studying a Master of Data Science at RMIT, and taking on selected freelance and contract engagements - pipelines, observability, backend APIs, cloud migrations.
[ Experience ]
Where I've been.
- Nov 2023 - Mar 2026
Data Engineer - UDE Central Monitoring
NTT DATA VDS · Hanoi, working with Wolfsburg DE team
Designed Azure-based ingestion, the LGTM stack and an Airflow + Azure Monitor anomaly detection pipeline. Migrated legacy PowerShell pipelines to Databricks.
- Feb 2023 - Oct 2023
Backend Developer - Licensight
NTT DATA VDS · Hanoi
Built a Quarkus backend with a hybrid MongoDB + PostgreSQL data layer for an enterprise licence intelligence product.
- Apr 2022 - Dec 2022
Data Engineer - Customer Data Platform
VCCorp · Hanoi
Designed HBase + Elasticsearch storage, Spring Boot APIs and Kafka queues. Wrote Scala Spark jobs for mass identity merging.
- Oct 2021 - Mar 2022
Data Analyst - Data Mining and Analysis
VCCorp · Hanoi
PySpark log analysis across Kenh14, Soha, Pega - handling 10TB/day. Set up Airflow DAGs for scheduled analyst workloads.
[ The stack ]
Tools of the trade.
Languages
- Python
- Java
- Scala
- JavaScript
- PowerShell
Frameworks
- FastAPI
- Flask
- Quart
- Spring
- Quarkus
- Node.js
- React
Data & ML
- Apache Spark
- Snowflake
- Airflow
- Databricks
- Kafka
- OpenCV
- Scikit-Learn
- TensorFlow
- Keras
Storage
- PostgreSQL
- MySQL
- MongoDB
- ElasticSearch
- HBase
- HDFS
- Firebase
Cloud & Ops
- Microsoft Azure
- AWS
- Docker
- Grafana
- LGTM Stack
- App Insights
[ Work ] - Index of 4 projects
Selected
work.
A small, considered set of projects spanning observability, backend APIs, customer data platforms and big-data analytics. Click any project for the full case study.
[ How I work ]
I obsess over the unglamorous parts of data - schemas, SLAs, retries, cost. The dashboards are the easy bit.
Listen first
I start by understanding the business, not the tech. Bad pipelines almost always trace back to a misread requirement.
Design for ops
Every system I ship has runbooks, dashboards and budgets. If it pages, you will know exactly what to do.
Boring is good
I default to proven tools - Postgres, Spark, Airflow - and bring novelty only where it pays for itself.
[ Contact ] - Let's build something
Say hello.
The fastest way to reach me is email. For longer briefs, the form below collects everything I need to give you a thoughtful first reply.