I've been a software developer professionally since 2015. I have a master's degree in data analytics which I worked on part-time because I found it interesting (April 2022), and a bachelor's degree in computer science (December 2014).
I currently live in Kitchener-Waterloo, Ontario, Canada, though I am originally from Poughkeepsie, New York, USA, and I also lived for several years in Denver, Colorado, and New York City.
You can contact me and view my complete work history on LinkedIn
My day job isn't to present publicly about my work, and much of what I've worked on has been closed-source and not externally visible - I do have a few presentations I can share, though.
A playful lightning talk about my hobbies, presented at PyCon US Lightning Talks, May 2024.
Slides and speaker notes.
How To Eliminate Surprises In Your Data was co-written and co-presented with my then-coworker Idrees Khan, about our work on Spotify's data quality infrastructure, much of which is open source.
Presented at Scale By The Bay, November 2019 (slides, speaker notes); Northeast Scala Symposium, March 2020 (slides).
Accepted to Scala Matsuri, June 2020; had to withdraw as travel from the US to Japan wasn’t possible in June 2020.
Abstract: How do you know you can trust the accuracy of the data flowing through a pipeline, and the insights derived from it? At Spotify, we have an infrastructure team focused on data quality to address this problem. From the cultural changes we’re making to give data engineers a quality mindset, to the specific tools we’ve written, we’ll explain how we increase confidence and eliminate surprises in our data contents, and how we approach problems in the wide space of ‘data quality.’ You’ll learn about a few key moments in the pipeline lifecycle when data quality might be compromised, and the approach we took to improving them.
Another playful lightning talk. Presented at Spotify-internal Data & Insights conference, May 2019.
The slides and demo code are available on my personal GitHub.