Matthew T is a product and engineering leader with 15 years of experience building and scaling data-driven consumer and B2B products, currently leading Recommendations at Credit Karma where his content-ranking work drove notable top-line growth across email and in-app channels. He has a track record of founding and scaling teams and products—from launching BigQuery Data Transfer and a Google Cloud marketing vertical to early engineering roles that 3x’ed SEM impact at Groupon and handled 200% YoY growth at Yelp. Matthew blends hands-on backend and cloud engineering (including adding Dataproc support to Yelp’s mrjob) with product strategy, growth and cross-functional alignment across 30+ person organizations. He founded Magelly to validate a travel marketplace concept and has repeatedly translated technical capability into commercial results by recruiting partners, defining go-to-market data products, and building operational infrastructure. Based in San Francisco, he pairs deep big-data and SEM expertise with entrepreneurial instincts and a proven ability to turn complex technical programs into measurable business outcomes.
15 years of coding experience
13 years of employment as a software developer
Saratoga High School
Master of Engineering (MEng), Electrical Engineering - Computer Science focus, Master of Engineering (MEng), Electrical Engineering - Computer Science focus at Princeton University
Saint Andrew's
National University of Singapore
Bachelor of Science (BS), Electrical Engineering - Computer Engineering option, Bachelor of Science (BS), Electrical Engineering - Computer Engineering option at University of California, Los Angeles
Run MapReduce jobs on Hadoop or Amazon Web Services
Role in this project:
Back-end & Cloud Engineer
Contributions:31 commits, 5 comments in 2 months
Contributions summary:Matthew contributed to the `mrjob` project by adding initial support for Google Dataproc, a cloud-based data processing service. Their work involved creating a new `dataproc.py` module with the necessary classes and methods to run MapReduce jobs on Dataproc. They addressed related code cleanups and merged upstream changes. The contributions involved setting up the infrastructure to run jobs on a Dataproc cluster, including managing temporary directories and file uploads to Google Cloud Storage.
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