Suyash Garg

Software Engineer

London, England, United Kingdom7 yrs 11 mos experience

Key Highlights

Stackforce AI infers this person is a Fintech professional with expertise in distributed systems and real-time data processing.

Amazon Web Services (aws)Apache Kafka

AlgorithmsCC++CSSCascading Style Sheets (CSS)Data MeshData StructuresFlaskFlinkGNU OctaveGNU/LinuxHTMLJavaScriptK8sLinux

Distributed Systems, Data and Machine Learning

Jul 2024 – Present · 1 yr 8 mos · London Area, United Kingdom

Sep 2022 – Aug 2024 · 1 yr 11 mos · London, England, United Kingdom

Amazon Web Services (AWS)

2 roles

Promoted

Mar 2022 – Sep 2022 · 6 mos · London, England, United Kingdom

Streaming Engine, DSLs, Multiple Clusters, Backups: Worked on handling infrastructure involving
stream-processing, messaging and logging, involving around technologies like Apache Kafka, Flink, Spring, K8s and Kafka. Developing & maintaining engine and infrastructure that provides an easy DSL for writing pipelines (over Kafka Streams & Flink) that handle complex real-time aggregations across different flows like CDD, AML, etc.
Operations, Migrations, Kafka on K8s, Reducing MTTR: Reduced recovery time of Kafka Offline Partitions
caused by network partitions & slow disks to < 5m by developing custom scripts. Handling migrations of incoming data from different services, helping push to a variant of Data Mesh for easy management of data across Wise.

Amazon Web Services (AWS)Apache KafkaFlinkSpringK8s

Feb 2021 – Mar 2022 · 1 yr 1 mo · London, England, United Kingdom

Jun 2019 – Sep 2019 · 3 mos

Mentored the project "GSoC 2019: Support for Reviewing and Rating More Entities on CritiqueBrainz" for adding support of reviews for more entities already present in the MusicBrainz (music-metadata) database. The amazing work done by the student Shamroy Pellew is described in the MetaBrainz blog

Jul 2018 – Jan 2021 · 2 yrs 6 mos · Berlin Area, Germany

Worked on Zalando’s Central Event Bus based on Kafka-like queues, enabling easy publishing, consumption & analysis of events at a very high scale (100+ TB/day transfer) through open source products, Nakadi and Nakadi SQL built on top of Apache Kafka & Kafka Streams.

Amazon Web Services (AWS)

May 2017 – Aug 2017 · 3 mos

◦ MusicBrainz database in CritiqueBrainz: Worked on accessing the music metadata database, MusicBrainz as a separate docker container in CritiqueBrainz, for speeding up the site. Formerly, CritiqueBrainz made use of the MusicBrainz Web Service for fetching entity information.
◦ Optimizing performance of CritiqueBrainz: Worked on optimizing site performance by mainly removing the SQLAlchemy ORM code and writing raw SQL statements for fetching data from the CritiqueBrainz database.
◦ Links: Contributions on Github: https://goo.gl/W2DWbt, Work done during GSoC: https://goo.gl/o1kkDA