Máté Karácsony
Senior Staff Software Engineer at Signifyd
Budapest, Hungary
Summary
👤
Senior🎓
Top SchoolMáté Karácsony is a Senior Staff Software Engineer with 14 years of experience blending academic research in source-to-source program transformations with hands-on engineering of large-scale data and distributed systems. He has led teams and technical strategy at companies like Ekata and Mastercard, delivering dramatic performance and size improvements in identity graph ingestion and blockchain data pipelines. Comfortable in functional languages (Haskell, Clojure) and Scala-based big data tooling, he has contributed to notable open-source work such as frameless for expressive Spark types, improving serialization and performance. Trained as a summa cum laude MSc and a former PhD researcher, he brings compiler and DSL expertise to production systems, often finding source-level optimizations that yield measurable runtime gains. Based in Budapest, he pairs deep technical craft with people leadership and a background that intriguingly includes formal brewing studies.
14 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD, unfinished), Computer Science, Programming Languages, Source-to-Source Transformations, Doctor of Philosophy (PhD, unfinished), Computer Science, Programming Languages, Source-to-Source Transformations at Eötvös Loránd UniversityBrewmaster, Brewmaster at Magyar Agrár- és Élettudományi Egyetem (MATE)
English, Hungarian
Github Skills (7)
serialization10
type-system10
data-serialization10
functional-programming10
spark10
scala10
scalatest9
Programming languages (9)
Github contributions (5)
typelevel/frameless
Feb 2018 - Apr 2018
Expressive types for Spark.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 commits, 4 PRs, 12 comments in 1 month
Contributions summary:Máté primarily focused on enhancing the `frameless` library, a Scala-based project for Spark. Their contributions centered around implementing support for unit types within the data encoding and serialization processes. They addressed serialization errors, added tests for unit-related functionalities, and optimized the code to improve performance, specifically by avoiding the use of `filter`. These changes involved modifications to the core `RecordEncoder` and `TypedEncoder` classes, as well as related test files.
functional-programmingspark-mlexpressivesparkscala
kmate/keg-scale
Aug 2022 - Sep 2024
Contributions:173 pushes, 1 branch in 2 years 1 month
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