| 2023/10-Present |
Software Developer |
|
Loblaw Digital |
|
- Designed and operated scalable and efficient backend services using Node.js and Java (Spring Boot), supporting over 300K daily users across pharmacy and health workflows. Built and maintained REST APIs powering search and profile services, contributing to high availability SLAs.
- Drove performance improvements across MySQL and Firestore by redesigning data access patterns, query structures, and indexing strategies, significantly reducing API latency under production traffic.
- Architected and maintained microservices-based backend components, ensuring reliable inter-service communication and scalable service deployment in a distributed environment.
- Enhanced system reliability and observability by expanding unit and integration testing, implementing structured logging and metrics, and contributing to incident response and root cause analysis for production systems; Led a monorepo migration across multiple services, reducing CI/CD pipeline time by 30% and improving build efficiency and deployment consistency across backend services.
- Delivered end-to-end features within an Agile team environment from design through production rollout by partnering with product managers, designers, and platform teams and applying analytical thinking, innovative solutioning and cross-functional communication skills; Leveraged GitHub, and Jira for effective project management, version control and team collaboration.
- Mentored junior engineers through code reviews and pairing, accelerating ramp-up and increasing overall team delivery capacity.
|
| 2023/03-2023/09 |
Software Engineer |
|
IpserLab |
|
- Developed backend components for web applications using Java and PostgreSQL, implementing REST APIs and backend services to support application functionality and data processing.
- Designed and optimized relational database schemas and queries in PostgreSQL to ensure efficient data storage and retrieval; Implemented unit testing and performance testing to maintain code quality and reliability of backend services.
- Contributed to backend feature development and system enhancements within an Agile engineering team, participating in API design, integration testing, and backend debugging.
|
| 2024/10 - 2024/12 |
Distributed Search Engine |
|
A distributed and cloud-based search engine inspired by Google |
|
Click here
to view this
project demo |
|
- This project is a distributed and cloud-based search engine inspired by Google, designed to deliver the most relevant search results based on user input in the search box; The search engine includes a custom-built web server (a Spark Java clone), a key-value store, a distributed analytics engine (an Apache Spark clone), a web crawler, an indexer and a page ranker.
- Designed and implemented distributed data processing pipelines to crawl, index, and rank 150,000+ web pages deployed across multiple Amazon EC2 instances. Implemented TF-IDF and PageRank algorithms to compute relevance scores and deliver efficient query results.
- Engineered backend services to support parallel crawling, indexing, and ranking, ensuring scalability and efficient distributed computation.
|
| 2023/06 - 2023/08 |
New York City Exploration Web Application |
|
A web application with Node.js and MySQL |
|
Click here to
view the project demo |
|
- Developed backend services using Node.js and Express.js to support a web platform for exploring Airbnb listings in New York City; Designed RESTful APIs to handle data retrieval, filtering, and search functionality for location and property datasets.
- Implemented MySQL database schema and query optimization to efficiently manage large datasets and improve backend response performance; Built backend logic to support data aggregation and dynamic query processing for location-based search results.
|
| 2023/04 - 2023/06 |
Climate Change and Human Health |
|
A data analysis project base on big data tools and machine learning models in Python and Pandas |
|
Click here to
view this project demo |
|
- Built project with big data tools and machine learning models in Python, pandas, numpy, scikit-learn, Spark and Dask to evaluate the relationship between climate change and human health by analyzing the datasets across 263 countries from 1960-2016;
- Conducted Data Loading, Preprocessing and Exploratory Data Analysis (EDA) on Temperature, GNI, Mortality rate, Life expectancy and population; Created data visualization by applying matplotlib and seaborn libraries. Trained and tested on key indicators by applying the Linear Regressions, Decision Tree Regression, Random Forest Regression and SVR models. To make forecast on time series datasets, implemented the ARIMA model to predict future trends in global temperature and associated health outcomes.
|
Programming Languages
Databases
Technologies/Frameworks