Paweł Tokaj
Staff Software Engineer at The Apache Software Foundation
Warsaw, Masovian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolPaweł Tokaj is a Staff Software Engineer with eight years of experience building scalable data and cloud systems, currently at Splunk and active as an Apache Sedona PMC member. He specializes in distributed systems, streaming, geospatial data processing at scale, and has deep hands-on experience across Spark, Kafka, Flink, Airflow and cloud platforms (GCP/AWS). As an active Apache committer he authored and enhanced the Python API for Apache Sedona, making a Java-based geospatial engine more accessible to Python users and improving serialization and usability. His background spans full-stack data engineering—backend services in Scala, Python and Go, data warehouses like BigQuery and Snowflake, and production tooling with Kubernetes, Terraform and CI/CD. Colleagues rely on him to bridge research-grade geospatial algorithms and production data pipelines, often solving interoperability challenges between big data and GIS stacks. Based in Warsaw, he combines academic training in geodesy with pragmatic engineering, which explains his uncommon depth in spatial data tooling and large-scale pipeline design.
8 years of coding experience
6 years of employment as a software developer
Warsaw University of Technology
English, norweski, Polish
Github Skills (8)
geospatial10
python10
dataframe9
jupyter-notebook9
dataframes9
javas8
scala8
java8
Programming languages (6)
Github contributions (5)
apache/sedona
Oct 2019 - Apr 2022
A cluster computing framework for processing large-scale geospatial data
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:46 reviews, 27 commits, 35 PRs in 2 years 5 months
Contributions summary:Paweł's contributions primarily revolve around enhancing the Python API for GeoSparkSQL within the `apache/sedona` repository. Their work involved adding core Python API features and integrating them with the existing framework. Furthermore, the user focused on improving the serialization process for GeoSpark Python, creating example notebooks, and updating code for broader platform support, suggesting a strong focus on usability and integration of the library. These changes suggest the user was responsible for making the existing Java-based geospatial library accessible through a user-friendly Python interface.
geospatialpythonspatial-analysisspatial-sqlcluster-computing
Imbruced/incubator-sedona
Dec 2020 - Nov 2022
A cluster computing framework for processing large-scale geospatial data
Contributions:4 PRs, 68 pushes, 16 branches in 1 year 11 months
wpspythongeospatialcluster-computingdask
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