AHAMED RILWAN

I've built end-to-end data platforms for logistics, e-commerce, stock-market streaming, and ML retraining using Kafka, Spark, Airflow, PostgreSQL, DuckDB, and Docker.

In my AI Logistics Streaming Platform, I simulated 500 drivers and more than 100K deliveries, achieving peak Kafka throughput of 890 messages per second and 99.8% processing efficiency. I also engineered schema validation, null monitoring, deduplication, and Airflow-based scheduling and monitoring.

My e-commerce platform processes 500K+ raw records through a four-layer Medallion Architecture, with idempotent batch pipelines, structured logging, and recoverability on failure. I've also built real-time transaction pipelines with Spark Structured Streaming, Delta Lake, and ClickHouse, plus ML feature engineering and automated retraining workflows tracked with MLflow and MinIO.

Alongside my data engineering projects, I completed a Business Analytics internship at Mustawa Sdn. Bhd. and published research on adaptive data structures selection. I'm pursuing a B.E. in Electrical and Electronics Engineering with a Minor in Data Science and want to apply my data engineering skills in a cloud-native environment.