Tornike Gurgenidze
Enterprise Data Architect
Georgia
Summary
🤩
Rockstar🎓
Top SchoolTornike Gurgenidze is an Enterprise Data Architect with 11 years of experience designing and delivering big data and feature-store solutions across leading Georgian banks. He combines hands-on backend development—maintaining Feast and contributing significant PySpark backend features to the popular Ibis project—with strategic data architecture and platform leadership. At TBC he progressed from Data Warehouse Developer to Team Lead and Senior Big Data Engineer before taking on enterprise-wide architecture at Bank of Georgia, driving scalable materialization and transformation pipelines. His open-source contributions include refactoring Feast’s Spark materialization to mapInPandas and introducing a gRPC registry server, reflecting a focus on performance and decoupled, production-ready ML feature tooling. Tornike pairs a business-administration background with deep technical fluency, enabling him to translate product requirements into auditable, high-throughput data systems. He’s notable for bridging bank-grade data governance with modern data engineering practices and active maintenance of community tooling.
11 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Business Administration and Management, General, Bachelor's degree, Business Administration and Management, General at Free University of Tbilisi
Github Skills (21)
feature-store10
python10
data-engineering10
dataframes10
pyspark10
dataframe10
sql10
grpc10
datatypes9
spark9
databases9
mlops9
database9
substr9
array-comparison8
Programming languages (6)
Github contributions (5)
feast-dev/feast
Jun 2023 - Mar 2025
The Open Source Feature Store for AI/ML
Role in this project:
Backend Developer
Backend DeveloperContributions:2 releases, 344 reviews, 169 PRs in 1 year 8 months
Contributions summary:Tornike primarily contributed to the backend functionality of the Feast feature store. Their work involved removing unnecessary dependencies, adding a gRPC registry server, and refactoring the Spark materialization engine to use mapInPandas. They also decoupled transformation types from on-demand feature views and added a substrait-based ODFV transformation.
pythondata-qualitydata-sciencemlmachine-learning
ibis-project/ibis
Mar 2023 - Sep 2024
the portable Python dataframe library
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 reviews, 4 PRs, 4 comments in 1 year 6 months
Contributions summary:Tornike primarily contributed to the backend of the ibis project, specifically focusing on enhancing the PySpark backend. They implemented array operations, including `ArrayFilter` and `ArrayMap`, and added partial support for interval types. Furthermore, the user introduced the `DateDiff` operation and addressed ruff errors within the PySpark tests. This work expands the capabilities of the PySpark backend, enabling more complex data manipulations.
polarspythondaskdataframesdata-science
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