Suyash Garg
Software Engineer at Stripe
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolSuyash Garg is a software engineer with 10 years of experience building scalable distributed systems, data pipelines, and machine learning infrastructure from London. He has driven streaming and event-driven architectures at companies like Wise and Zalando—working on Kafka-based event buses processing 100+ TB/day—and now contributes to infrastructure at Stripe. Suyash’s work spans backend engineering, data engineering with Spark, and operational reliability (including reducing Kafka recovery time to under five minutes), demonstrating both deep systems expertise and pragmatic tooling. He is an active open-source contributor (notably to Zalando’s Nakadi) and mentors community projects, reflecting a habit of “learning one bit at a time” through code and collaboration.
10 years of coding experience
6 years of employment as a software developer
Engineer's Degree Computer Science, Engineer's Degree Computer Science at National Institute of Technology Hamirpur
English, Hindi, Telugu, Marathi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (20)
spark10
python10
back-end-development10
data-engineering10
big-data10
java10
javas10
eventbus10
spring-boot10
api-design9
restful-api9
api-rest9
databases9
database9
rest-api9
Programming languages (10)
Github contributions (5)
zalando/nakadi
Jul 2018 - Oct 2020
A distributed event bus that implements a RESTful API abstraction on top of Kafka-like queues
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 1 review, 201 commits in 2 years 2 months
Contributions summary:Suyash's contributions focused on removing legacy feature toggles throughout the codebase, specifically in the `EventStreamController`, `EventTypeControllerTestCase`, `EventTypeService`, `EventStreamWriterProvider`, and `PostSubscriptionController`. These changes aimed to reduce technical debt and streamline the development process. Further commits involved refactoring and removing tests related to those feature toggles, as well as checking and removing more toggles. The user also made changes to ensure correct handling of invalid events and schema updates.
queuesjava-8streamsmessage-passingevent-driven
metabrainz/listenbrainz-server
Dec 2016 - Apr 2018
Server for the ListenBrainz project, including the front-end (javascript/react) code that it serves and all of the data processing components that LB uses.
Role in this project:Back-end Developer & Data Engineer
Back-end Developer & Data EngineerContributions:9 commits, 1 PR, 1 comment in 1 year 3 months
Contributions summary:Suyash primarily contributed to the backend data processing components of the ListenBrainz server. They developed functions in Python using Spark to load and process data from ListenBrainz dumps, creating RDDs and DataFrames for user and recording information. The user also implemented functions to prepare and store data in persistent tables, focusing on data transformation and storage for a music recommendation system. Their work included creating the core data structures and pipelines necessary for subsequent analysis and model training.
reactpythondata-processingjavascriptbig-data
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