Cuong Dang
Verified Expert in Engineering
Data Engineering Developer
Waverly, IA, United States
Toptal member since November 14, 2022
Cuong is a well-rounded data engineer who delivers complete solution packages, including design, code, documentation, testing, and infrastructure. He is passionate about theory and practice and commits to lifelong learning, especially in computer science and mathematics. One of Cuong's highlightable experiences includes a scalable system that migrated data from on-premise databases to a cloud data lake. He is also proficient in back-end development.
Portfolio
Experience
- SQL - 5 years
- Python - 3 years
- Scala - 2 years
- Data Visualization - 2 years
- Spark - 2 years
- Data Engineering - 2 years
- Terraform - 1 year
- C - 1 year
Availability
Preferred Environment
Scala, SQL, AWS Step Functions, Terraform, Spark, AWS Glue, AWS Lambda, Bash
The most amazing...
...system I've designed and implemented is a data system migrating thousands of tables from on-premise to the cloud for one of the largest banks in the country.
Work Experience
Senior Data Engineer
Curvo Labs
- Developed and maintained the company's data platform, including core processing and serving pipelines with all infrastructure as code.
- Designed and developed a nonlinear regression with a Spark data pipeline to calculate potential savings likelihoods.
- Created back-end APIs for product features and standard libraries for other developers.
Data Engineer
Techcombank
- Designed and implemented a system that migrated data from on-premise databases to a cloud data lake that scaled to the scheduled pulling of thousands of tables with different change-data-capture patterns and throughput requirements.
- Created operations processes and implemented validation for ad-hoc file upload.
- Gathered requirements; designed and built an end-to-end BI performance tracking dashboard that ingested and processed data from multiple heterogeneous sources.
Experience
On-premise to Cloud Data Migration System
The goal was to maximize automation that allows Data Operations staff to add source data tables to be loaded quickly. The system was composed of the main components:
• Directed Acyclic Graph (DAG) Generator—induced Airflow DAGs.
• Extractor—pulled data from sources based on different specified change-data-capture (CDC) patterns.
• Uploader—picked up data from buffer storage and loaded them to the cloud landing zone.
• Validator— validated files in file upload jobs.
Education
Bachelor's Degree in Economics
Yonsei University - Seoul, South Korea
Skills
Tools
AWS Step Functions, AWS Glue, Microsoft Power BI, CircleCI, Git, Terraform, Apache Airflow, Apache Beam
Languages
Scala, TypeScript, Python, SQL, Java, C, Scheme
Frameworks
Spark, NestJS
Platforms
AWS Lambda, Docker
Storage
PostgreSQL, JanusGraph
Other
Data Engineering, Data Visualization
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