Wu Wenhan
Data Developer
Shanghai, Shanghai, China
Summary
👤
Senior🎓
Top SchoolWu Wenhan is a data developer with eight years’ experience building robust data and risk systems in finance and research environments, currently enhancing Nomura’s ETL, CVA, and disaster recovery capabilities. He combines backend engineering in Python and Java with hands-on use of Prefect, Pandas, Jenkins, and ServiceNow integrations to keep critical systems resilient and auditable. His contributions to the open-source OpenMLDB project—adding built-in functions and fixing cross-module issues—showcase practical database and batch-processing expertise beyond his day job. With a Master’s in Software Engineering from East China Normal University, he brings strong foundations in databases and front-end technologies that help bridge analytics and production pipelines. Colleagues rely on him to solve thorny system continuity problems and translate complex financial requirements into reliable deliverables. He’s motivated by tackling high-impact challenges where data integrity and uptime matter most.
8 years of coding experience
3 years of employment as a software developer
Master's degree, Master's degree at East China Normal University
Github Skills (11)
javas10
databases10
relational-databases10
sql-database10
java10
database10
feature-engineering9
sql9
c-language8
jdbc8
cprogramming-language8
Programming languages (3)
Github contributions (5)
4paradigm/OpenMLDB
May 2022 - May 2022
OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:27 reviews, 6 commits, 6 PRs in 8 days
Contributions summary:Wu primarily contributed to bug fixes and feature implementations within the OpenMLDB project. Their work included resolving compiling warnings, fixing Javadoc warnings in the JDBC module, and adding support for new built-in functions such as `radians()`, `char_length()`, `character_length()`, and `char(int)`. These changes spanned multiple areas of the project, demonstrating involvement in both core functionality and database features. The user also addressed a compile warning in the batch processing module.
machine-learning-databasetrainingfeature-storein-memory-databasefeature-extraction
Ivyee17/ivyee17.github.io
Dec 2018 - Jan 2023
Contributions:4 releases, 100 commits, 69 pushes in 4 years 1 month
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