I discovered my passion for data science through research I completed in oceanographic physics at the University of Washington. Since then, I have been working in industry at companies such as Nike, LinkedIn, and Pave to bring world-class data products to life.
Data Engineering. Data Science. Software Development.
I am a technology
generalist specializing in data engineering.
April 2023 - Present
- Led the development of multi-million dollar data products that are core to the business using Python, SQL, dbt & Google Cloud.
- Created throughput metrics to identify issues related to data dropout in the dbt pipeline.
- Created metrics dashboards to identify data quantity and quality issues related to product releases and updates.
November 2021 - March 2023
- Independently led and completed a 10-month project to migrate logic from Hive to Spark using Spark Scala and Spark SQL in a daily ETL pipeline using technologies similar to Airflow. This project impacts sales operations internationally on hundreds of millions of dollars in revenue to help identify the best sales representative for a given sales opportunity. Results include saving hundreds of hours per year of manual effort across 5 teams at LinkedIn, removing frequent incidental data loss in the pipeline, and improving the runtime of the ETL pipeline significantly.
- Partnered closely with business teams to provide enhancements to ETL pipelines using Spark SQL, Spark Scala and Python.
- Independently addressed on-call issues, often leading triaging efforts across several teams operating internationally.
- Improved team's on-call processes via tutorials using Jupyter Notebook and SQL.
- Created documentation to guide decisions made at the data organization level on which technologies to use to create a new data pipeline.
August 2019 - October 2021
October 2015 - June 2019
Led research analytics for a three and half year project to analyze variabilities and inconsistincies in the world's best climate models. Analysis was performed using Python. Work was funded by NASA and the NSF.
October 2018 - March 2019
Created two new undergraduate data science courses focusing on beginning and intermediate data science techniques in the field of oceanography. Courses were taught in Python, with a focus on the basics of numpy and pandas.
In UC Berkeley's Master of Information and Data Science (MIDS) program, I analyzed Bay Wheels
bikeshare data to drive business growth.
Analysis with SQL, Python, BigQuery, Jupyter Notebook. Link
In UC Berkeley's Master of Information and Data Science (MIDS) program, my team and I extensively analyzed the relationship
between mobility and the number of COVID-19 cases per capita in each US state.
Analysis in R. Link
At the University of Washington I created and taught undergraduate
classes (Github) on how to
create things like this.
Content included Python, numpy, matplotlib and more.
Data Source: World Ocean Atlas 2018
Now I use deep neural networks and machine learning to help bring ideas to life. Test out the following digit recognizer as an example (Github), which uses a convolutional neural network I built in Tensorflow (99.3% accurate on MNIST dataset). UI Credit: Benson Ruan
I love to talk about life, work ethic, and professional philosophies.
During a time when we are spending more time than ever at home, our habits here are more important than ever. Here, I discuss books that have helped shape what I think of as some of the best habits in my life. Post.
Through my studies in oceanography at UW, I personally came to define oceanography as the study of the physical, biological, chemical, and geological factors of the ocean and how these components interact. And when you are talking about doing this with something that covers ~75% of the globe, it’s quite hard to do that by hand. Post.
Please use the links below to get in touch.