I am a data engineer with hands-on experience building production-ready ETL and real-time data processing pipelines.
I work with large-scale data integration, transforming data from sources such as Kafka, S3, SFTP, JDBC, APIs, and log streams into reliable analytics-ready assets.
My background includes strong experience in PySpark, SQL, data modeling, and workflow automation, along with practical use of cloud and containerized environments.
I have also worked on ML-oriented data workflows, anomaly detection, and automated data health monitoring to improve data quality and operational reliability.
I enjoy collaborating with stakeholders to translate business needs into technical solutions and to optimize systems for performance and maintainability.
I am open to opportunities where I can contribute to data engineering, analytics platforms, and scalable data infrastructure.
Developed production-ready ETL pipelines using PySpark to integrate large volumes of data from sources such as S3, Kafka, SFTP, JDBC, and APIs into high-quality data assets. Designed ML workflows for clustering and classification, optimized Spark jobs, built data health monitoring, and supported incident management for data delivery failures.
Designed and implemented a real-time log processing pipeline using syslog-ng, Kafka, PySpark, and MongoDB. Built an anomaly detection workflow for network logs using Wazuh, n8n, and integrated LLMs for context-aware analysis.
Customized Backtrader for Vietnamese market regulations, developed Python-based data ingestion using APIs, BeautifulSoup, and Selenium, stored data in MongoDB, and built a FastAPI microservice with a real-time signal execution engine using Redis caching.
Bachelor of Science, Data Science
GPA: 8.68/10. Relevant coursework included Machine Learning, Big Data Analytics, Data Analysis and Visualization, Data Warehousing and OLAP, Database Systems, Object-Oriented Programming, and Data Structures and Algorithms.
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