Designed and implemented graph database to speed up near real time BI query and to support multi-degree data integration

Developed, monitored and maintained scalable cross-platform ETL data pipeline scheduled by Airflow.

Built dashboard for database quality check and near real time integration level monitoring and analysis

Design graph algorithm query to detect fraud relationship between fraud merchants and fake users for anti-fraud purpose

Designed analysis tool based on NLP for transaction comment to support fraud detection and context management

Implemented machine learning model and NLP algorithm to build classifier for comments sentiment analysis, fraud detection and topic identification.

Built ETL data pipeline scheduled by Airflow for automatic data processing and results storage

Achieved 88% accuracy on sentiment analysis and 100% accuracy on top 5% merchant level fraud detection