Software Developer in Kirkland, WA, United States

I have been working with a large scale software system that has a total of 14 million lines of code (C#/C++) for 11 years. It used to take 8-10 hours to build and generate the installation kit or deploy it to the cloud. With the old baseline build, it could only generate one build and one kit per day.

We built the CI (continuous integration) system in Python mainly and also used Powershell, Jenkins, and other technologies to implement automated processes in the development cycle.  After setting up the CI system, the average build time is about an hour and installation kit generation/deployment takes about 1-1.5 hours.

We also introduced many code metrics such as compile warnings, code complexity, and unit test coverage into the CI system. In addition, the CI system also helps to run all kinds of automation testing and generate reports. The CI system improved development efficiency for about 200 developers, QAs, and managers.

I redesigned many complicated classes in a large-scale C++ codebase following different design patterns, such as singleton, factory method, composite, adapter, or observer. The C++ codebase was refactored to make it testable by extracting to interfaces. I used GTest/Gmock provided by Google to test our legacy C++ class. 

As a large-scale real-time acquisition system, we set up a central place to store logs, performance data, dump files, etc., which are collected from different desktops or public cloud deployments.

We developed a web service to provide automated processes for different purposes, such as log file analysis to find the most popular tool string combination, performance data analysis to find out potential performance bottlenecks; dump file analysis to find the root cause of a crash or exception. We use C# with .NET Core and Python to develop the back end and Node.js and JavaScript to develop the front end.

SQL Server and Cosmos DB NoSQL as DB were implemented for data storage, and Redis cache and memory cache as another layer of data storage to achieve better performance. This web service was deployed to Azure.

I designed and implemented the REST APIs/endpoints for a payment workflow serving as an e-payment web service or serving a mobile app. The web service runs in Azure and communicates with different payment service providers to finish payments. It is built on top of C#/ASP.NET/.NET Core. It utilized different layers of data storage: SQL Server, Cosmos, Redis Cache, and an in-memory cache in different stages of the whole workflow to achieve the best performance.

We built different web pages with Node.js and Angular. Those web pages can serve developer troubleshooting purposes, statistics, and reporting purposes for managers and operation stuffers, testing REST APIs and making demos for clients, etc.

I developed a web service to provide different format files (such as DLis, or PDF) as the client requires as deliveries of a large real-time acquisition system. I used Java as a back-end coding language to support a web service running in AWS as well as Cassandra as DB and AWS S3 as data storage, Node.js for the front end to set up a simple UI and do API testing, and TDD applied through the whole lifecycle of the project.

I built a 3G device management system running in Unix for Nortel Networks. That system was developed by Java together with a small part of a C++ component. The C++ code mainly communicates with 3G devices via socket connection based on special protocol.

Java code builds whole systems based on MVC include UI, business logic, and data model. I used JNI to connect Java MVC parts with C++ components so that we can get and set configuration of 3G devices from the UI all the way down to hardware.

Rewrite a Java based REST API service using C#/.Net core. This REST API server doing following things:
1. Receive xml request from financial organization
2. Deserialize the raw xml request to specific data model based on different request type in xml
3. Apply different logic to convert data model in different workflows
4. Serialize the final data model to xml and send back