Wenfeng Pan

Production Engineer


Hey, I’m Wenfeng!

Nice to meet you!

🎙️ 👨‍🍳 🎮 🎦


  • Software Development
  • Site Reliability Engineering


  • Master of Computer Science, 2018

    University at Buffalo

  • Bachelor of Software Engineering, 2014

    Qingdao University


Python + Django

Java + Spring

Linux / Unix

Docker / Kubernetes

PostgreSQL / MongoDB

RabbitMQ / Redis

Vue.js / React

Amazon Web Services




Production Engineer


Mar 2020 – Present Menlo Park, CA

  • Developed and maintained infrastructure for some internal tools which uses widely across the company.

Software Engineer, Systems

Electronic Arts

May 2019 – Aug 2019 Austin, TX

  • Built an asynchronous delivery request tool using Python, Django, Flask, React and MongoDB, which simplified the process of infrastructure deployment
  • Implemented a RESTful microservices architecture using RabbitMQ Publish–subscribe pattern to expedite the process of building game servers to 20% faster and scalable to meet future needs
  • Completed technical system design, coding development, testing, deployed for intern project and conducted tech talks on service

Software Engineer Intern

ifanr Inc.

Mar 2018 – Aug 2018 Guangzhou, China

  • Participated development of a BaaS Serverless Platform similar to AWS Lambda, which provided high-quality services for 50,000 developers and 80,000 applications every day
  • Migrated the basic authorization login method to Single Sign On (SSO), which reduced 15% server load
  • Used Python to maintain an old version Twitter Bot, which published article automatically


Amazon Dynamo Style Key-value Store

Implemented distributed storage system using Android application, supported basic object actions and membership maintenance; Implemented a cycle replication system for key-value partitioning using SHA-1 function; Designed object versioning system and used Merkel tree for resynchronization after failures; Satisfied consistency and availability of CAP theorem for all clients in the system


A project can extract facial landmark and evaluate the beauty of face, which is inspired by a book Computer Models for Facial Beauty Analysis; This project use The Vertical Thirds and Horizontal Fifths algorithm to evaluate the beauty of the photos

Beijing Housing Price Prediction

Showed data virtualization by Pandas, NumPy, matplotlib, and displayed the performance of the decision tree; Input 10,000 groups data of housing price as a practice, and achieved 78.92% model performance

Raspberry Pi Live Streaming

Implemented the online streaming system with Node.js, WebSocket and FFmpeg; Used Raspberry Pi as server, support for streaming up to 100 clients