Matt Buck
Quantitative Platform Engineering
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolMatt Buck is a quantitative platform engineer with eight years of experience building cloud-native, data-driven backends and APIs using Python, PostgreSQL, Redis, and AWS. He combines deep database expertise—indexing, JSONB, full-text search, partitioning and query optimization—with event-driven systems design (Redis pub/sub, Kinesis) to deliver scalable financial and recommendation platforms. His work spans platform engineering, containerized model deployment, and production ML pipelines—he pushed a Dockerized recommender to AWS that integrated predictions into SQL and created a PostgreSQL-based user relationship graph aggregated with Pandas. Comfortable across the stack, Matt also develops trading strategies, backtests with Python and PineScript, and builds observability tooling (Prometheus/Grafana) to support engineers. Based in New York, he brings a practical blend of data science, statistics, and systems engineering that surfaces non-obvious insights from large text corpora and high-throughput event streams.
8 years of coding experience
7 years of employment as a software developer
Computer Science, Computer Science at NC State UniversityData Science, Data Science at Metis
Github Skills (2)
paginator9
django6
Programming languages (2)
Github contributions (5)
mblance/django-paginator-rawqueryset
Nov 2017 - Sep 2018
A django paginator for RawQuerySet
Contributions:16 commits, 4 PRs, 18 pushes in 10 months
djangopaginator
mblance/aws-nginx-example
Dec 2017 - Oct 2019
Contributions:4 pushes, 1 branch in 1 year 10 months
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