▪ Design, develop, and maintain robust system for the front-end credit-card servicing webpage and backend credit card database of our payment systems, as well as implementing new features and functionalities that enhance user experience and drive business growth on the branded credit card business model. For example, I designed system to support users to hold multiple credit card with different types of reward and spending system, which include front-end webpage based on Html, Javascript, css and React framework, as well as database backend based on Go and SQL.

▪ Use Firebase remote feature flags to conducted experiments to track and study different user actions, as well as to find the best set of interface for the best user engagement and product usage based on data analysis on user data. And choose between feature launch candidates to select the best one to bring the most business value.

▪ Combine engineering skills with innovative business ideas to create new features for our payment system. For instance, I introduced a credit card recommendation feature based on user transaction history, which resulted in increased user engagement, retention and credit card usage. By leveraging my technical expertise and understanding of user behavior, I regularly identified opportunities to enhance our system's functionality and drive further business growth.

Worked on deep-learning based dish recommendation system for restaurants.

▪ On Omnibox search for Android Chrome team.

▪ Designed and implemented the Chrome Omnibox (top search box) design revamp, boosted daily active Google search count from Android platform by ~0.5%.

▪ Designed and implemented the rich suggestions for Chrome search, which introduces the suggestions from other 1st party apps into Chrome search. For example, searching “Bad Guys” on Chrome will show you results on top MV from Youtube. More accurate search results and faster navigation to other apps decreased ~3% of user time to search destination.

▪ Conducted experiment data analysis to find user engagement and system performance difference between feature launch candidates and made decision to select the best launch candidate for browser UX/backend change. Example experiment include Chrome Omnibox UX revamp, performance upgrade with Android recycled view, status bar color synchronization etc.

▪ Chromium Open Source Code Location: https://chromium-review.googlesource.com/q/owner:rongtan@google.com

▪ Designed, implemented and maintained the data pipeline to fetch, store, update, and serve COVID-19 statistics on Bing.com, including case number, vaccination number, useful information, etc. for all global regions

□ The backend of the data pipeline includes a SQL database that contains the case and vaccine case data for all the countries from different sources, and a dedicated view to collect the latest and time series data hourly by ranking data and source validity. It also includes different other useful information such as government regulation, vaccine phases for different countries in Object Store NoSql database

□ The middle-layer part of the pipeline, based on C#, serves the Bing front-end with the latest data from

the SQL database as well as NoSql databases, with data normalization, aggregation, and formatting

▪ Designed and implemented the front-end UX for election modules on Bing First page result module and MSN

election hub by using React framework and front-end languages including scss, cshtml and Typescript

▪ Designed the data schema with backend together to ensure smooth data transfer. And the election modules all include dedicate unit tests to ensure zero incident throughout the all election phases.

□ The MSN modules include description, parties, candidates, coalition calculator, etc.

□ The Bing modules include election results, parties, candidates, etc.

□ The modules all include extra accessibility features including screenreader, keyboard access, etc.

▪ Designed, implemented and maintained the mid-layer data pipeline on Microsoft Falcon micro-service platform to fetch and serve recipe entity information for the MSN Recipe service. The data pipeline receives the front-end request, retrieves the Bing recipe entities from Recommendation system and Bing workflow, and sends the parsed recipe entities to the front-end with data normalization, aggregation, and formatting

▪ Work in C++ with the Broadway trading team to fix defective incidents for Broadway’s distributed automated trading platform, including Slow Crypto Currency Confirmation, Initially Disabled Market Credential Problem, etc.

▪ Develop Python test cases to reproduce errors from defective incidents, propose potential fixes and deliver completed trading system fix in C++ in complete software development cycles

▪ Design and implement Python auto testing module to compare latency between different releases of Market Aggregator