Gleb Kanterov
Staff Engineer at Databricks
Stockholm County, Sweden
Summary
🤩
Rockstar🎓
Top SchoolGleb Kanterov is a Staff Engineer with 16 years of experience specializing in Scala and Java for large-scale data and backend engineering, currently shaping Jobs and Workflows at Databricks. He previously led a team at Spotify building a petabyte-scale OLAP warehouse and self-service platform that cut production errors by 95% through static SQL analysis and orchestration at millions of executions per day. An Apache Beam committer and active contributor to prominent projects like Apache Beam, Scio and Typelevel libraries, he has hands-on expertise in serialization, nullability handling in SQL, and performance-sensitive distributed systems. Gleb blends deep functional programming and algorithmic knowledge with product-minded leadership, designing systems that reduce operational toil and cost. He also has a track record of building developer tooling and novel prototypes (including a Datomic-like browser DB) that reveal a propensity for ambitious, cross-stack engineering. Based in Stockholm, he combines research-grade rigor from NSU with pragmatic delivery across cloud-native and streaming ecosystems.
16 years of coding experience
11 years of employment as a software developer
Bachelor, Computer Science, Bachelor, Computer Science at Novosibirsk State University (NSU)Masters, Computer Science, Masters, Computer Science at Novosibirsk State University
Russian, English
Stackoverflow
11reputation
1kreached
1answer
0questions
Github Skills (38)
apache-calcite10
spark10
typesc10
testing10
data-serialization10
java10
calc10
serialization10
scala10
datasets10
stream10
javas10
fs10
f10
sql10
Programming languages (12)
Github contributions (5)
typelevel/frameless
Jan 2016 - Apr 2017
Expressive types for Spark.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:43 commits, 25 PRs, 14 pushes in 1 year 3 months
Contributions summary:Gleb primarily contributed to the `frameless` project by implementing and extending the core functionalities related to typed datasets and encoders. Their work involved defining `TypedEncoder` implementations for various primitive types, and adding functionality to create datasets from RDDs and to perform joins. They also added a new vector encoder, and implemented features like `mapGroups` and `flatMapGroups`. The user also contributed code for aggregate functions and widening and casting for different data types.
functional-programmingspark-mlexpressivesparkscala
apache/beam
Jul 2018 - Oct 2020
Apache Beam is a unified programming model for Batch and Streaming data processing.
Role in this project:Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:4 reviews, 68 commits, 64 PRs in 2 years 2 months
Contributions summary:Gleb contributed to fixing aggregation of null values in SQL queries, specifically within the context of Apache Beam's SQL implementation. Their work involved propagating nullability information to the Calcite data types, making aggregate functions handle nulls correctly, and adding integration tests. Furthermore, the user addressed issues related to schema nullability in output schemas, particularly concerning "group by" and "join" operations, as well as fixing the handling of byte arrays.
golangpythonstreaming-databeambatch
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