• Designed and implemented a static analysis pipeline for nightly and release builds, used for tracking API breaking changes and dependency upgrades, checking symbol conflicts and generating dependency lists in release notes

• Built a new pipeline to synchronize internal Spark fork with the company's monorepo, reduced the latency to integrate new code changes from days to less than 3 hours. Improved monitoring by adding dashboards and alerts for out of sync

• Developed and deployed the pipeline for updating aarch64 images, enables the product on ARM-based instance

• Coordinated the important dependency update from Hadoop 2 to Hadoop 3 during Databricks Runtime 9 to 10 major release

• Contributed several user experience improvements to Spark History Server, a debugging tool for Spark jobs

• Contributed to several optimization passes in the query compiler, like common subexpression elimination

• Removed all old log4j dependencies and replaced them with reload4j project, helped mitigate log4j vulnerabilities

• Built a new pipeline to annotate network traffic metrics with service and hardware information, and boosted query speed by 2x through forcing Presto to use broadcast join in physical plan

• Designed and developed a tool in Python to search for adjustments of service capacity placement among data centers which can reduce cross-datacenter network traffic, and proposed several suggestions which each can reduce the cross-datacenter network traffic by 12%(several Tb/s) with the help of that tool

• Designed and implemented a distributed storage system for large-scale computer vision datasets with Go

• Improved the service monitoring system by adding more detailed metrics about usage patterns for better schedule policy

• Boosted VSCode Arduino extension’s building speed by 8.25x with incremental build feature, reduced average build time from 1.5 minutes to 10 seconds

• Provided insights of VSCode user activity data using machine learning clustering algorithms