# Kevin Wang > Software Engineer Location: New York, New York, United States Profile: https://flows.cv/kevinwang4 ## Work Experience ### Software Engineer @ Datadog Jan 2024 – Present ### Co-Founder @ Coo Jan 2024 – Jan 2025 | New York, New York, United States Built and launched a local clothing resale marketplace in NYC from 0 to 1, live on the App Store since August 2024. Learned a ton about React Native, two-sided marketplaces, the cold start problem, and the clothing resale industry. ### Software Engineer III @ Dataminr Jan 2022 – Jan 2024 | New York City Metropolitan Area - Led scaling Dataminr's core Alert datastore on Elasticsearch onto a faster, cheaper, and self-managed hot-warm-cold index to accommodate 5x additional scale. - Reduced the E2E latency of user’s alert delivery settings changes by 5x by drastically reducing Postgres transaction contention. This decreased the time it took to fully reindex permissions changes for all Dataminr customers from 6h → 1.2h. - Made significant cost cuts to the teams AWS bill, without impacting performance. - Fixed complex Java concurrency bugs and connection leaks leading to frequent customer impacts. - Developed thorough testing framework for the team’s APIs, leading to the discovery of numerous bugs. ### Senior Member Of Technical Staff @ Salesforce Jan 2018 – Jan 2022 - Designed and built ETL pipeline using Hive and Airflow to identify data reclamation opportunities in Salesforce's blob storage cluster (13000 node fleet, 145 PB). With a looming capacity shortage, this tool enabled iterating on analytics data from parsing 16 Tb of log data each day for rapid hypothesis testing. Uncovered a total of 6 months worth of reclaimable storage runway when there was only 3 months of runway left, and reduced additional capacity costs by $4 million+ in 2020. - Developed scalable Airflow as a Service platform. Built flexible, secure deployment pipelines using docker/kubernetes/spinnaker/terraform for local, stage, and production environments. Successfully onboarded 4 teams to Salesforce Airflow, each requiring it’s own set of integrations ranging from Hive, Presto, Jupyter notebooks, SQS, Spark, and other internal tools. - Built metrics producer code to send data to Kafka from 30+ teams across Salesforce infrastructure, producing 12 million+ meaningful events per hour. - Developed real time and batched monitoring applications using NodeJS APIs and React/D3 visualizations for infrastructure teams. Also implemented these monitoring and alerting tools internally for operational management and analysis of our team's rapidly growing ElasticSearch, Cassandra, and Argus (internal TSDB) clusters. ### Software Engineering Intern - Infrastructure @ Salesforce Jan 2017 – Jan 2017 | Indianapolis, Indiana Area Developed alerting mechanisms and pipelines for the Marketing Cloud's API driven database. Contributed to the 3 phase development of a monitored, automated, and self recovering database ecosystem. These tools to drastically reduce response and recovery times for incidents at Salesforce datacenters around the world. ### Software Engineering Intern @ j2 Global Jan 2016 – Jan 2016 | Hollywood, CA J2 rapidly acquires software companies with similar technology, and integrates the code into J2's existing infrastructure. During my internship, I worked with the mobile apps team to build the FaxDocument Android app. I implemented marketing analytics and testing tools - Firebase, Leanplum, and Google Analytics. ### Research Intern @ USC Media Communications Lab Jan 2014 – Jan 2016 | Los Angeles While current systems can detect copied images, it is much more difficult to discover altered (Photoshopped) images. With Professor Jay C.C. Kuo, I created various tests for media forensics algorithms to detect image and video forgery in a defense contract issued by DARPA. I worked to increase the quality and efficiency of video streams, Netflix has funded research to develop an image compression algorithm called JND. I organized and conducted several subjective tests to collect key image compression and quality differentiation data. With PHD students Hao Xu, Arjun Pola, and Qin Huang, I am developing features for a cutting edge, high-accuracy, object detection algorithm that builds off of the C++ based Caffe deep learning framework. We use NVidia's K40 processor to train CNN architectures so that we can further research the features trained by Caffe. ### Printed Circuit Board Designer @ Tubis Technologies Jan 2013 – Jan 2013 | Pasadena, California 20 to 30% of the world's rural areas are uncovered by cell-phone coverage. Constructing the infrastructure to cover the middle-east is currently extremely cost-inefficient, so cell companies have no plans to develop coverage in these areas. The tablet sized device developed by Tubis Technologies, has the potential to provide coverage to all of these areas. I designed and prototyped a high efficiency printed circuit board to deliver DC to 54 antennas in an iPad sized receiver. Applying principles used in designing bulk transmission networks, I was able to minimize the power loss by strategically placing the AC-DC converters less than 1 centimeter away from each antenna. This reduces the distance over which the power is transferred over high current and low voltage (the form of power that the antenna requires). The design is scheduled to go into production in Fall 2018. ## Education ### Bachelor’s Degree in Computer Engineering and Computer Science, Minor in Business Finance University of Southern California ### High School San Marino High School ## Contact & Social - LinkedIn: https://linkedin.com/in/kevinwang1996 --- Source: https://flows.cv/kevinwang4 JSON Resume: https://flows.cv/kevinwang4/resume.json Last updated: 2026-04-05