Data Engineer & Analytics specialist. Building end-to-end pipelines, real-time IoT systems, and scalable cloud architectures that turn raw sensor data into actionable intelligence.
I'm Wai Yan Min Khant, a data engineer based in Kuala Lumpur, Malaysia. I specialize in Data Science and Business Analytics with a strong foundation in computing, currently pursuing my MSc at Asia Pacific University.
I'm passionate about transforming large-scale, high-frequency data into secure, real-time insights and trainable datasets that drive decisions.
End-to-end ETL & streaming pipelines using MQTT, Node-RED, and InfluxDB for high-frequency IoT sensor data.
Predictive models using Random Forest, Gradient Boosting, CNNs, and transfer learning for classification tasks.
Scalable, secure AWS infrastructures (EC2, S3, RDS, VPC, IAM) for large-scale analytical research workloads.
Live monitoring dashboards in Grafana with geomapping, trend analysis, and anomaly detection alerts.
End-to-end IoT monitoring system for temperature-sensitive logistics vehicles, enabling proactive anomaly detection and live route tracking.
End-to-end predictive analytics to identify key drivers of airline customer satisfaction.
Random Forest & Gradient Boosting models for airline satisfaction prediction using ensemble techniques.
Scalable, secure multi-region AWS infrastructure designed for large-scale research analytics workloads.
Deep learning CNN model classifying 101 food categories using transfer learning on the Food-101 dataset.
ML solution automating loan approval decisions using financial and customer data.
Full-stack Java EE web application with integrated ETL processes and real-time analytics dashboard.
I'm open to data engineering roles, freelance projects, and research collaborations. Feel free to reach out through any channel below.