Martin Andersson
Data Engineer
Gothenburg, Västra Götaland County, Sweden
Summary
👤
Senior🎓
Top SchoolMartin Andersson is a pragmatic data engineer with nine years of experience building data-intensive back-end systems, specializing in Java and Apache Spark-based pipelines. He has applied his skills across finance and telecom, delivering scalable ETL and analytics solutions using Apache Sedona, Luigi, PostgreSQL/PostGIS and Kafka. Martin is a hands-on contributor to Apache Sedona—implementing SQL functions, adding null/empty-geometry safety, and optimizing joins—which highlights his deep interest in geospatial processing performance. At Kodkamraterna and Telia he focused on data modeling and distributed processing, while earlier roles combined Spring Boot microservices and enterprise databases. He pairs solid engineering discipline with practical leadership as a former ScrumMaster and board member, able to translate complex data requirements into reliable production systems. Based in Gothenburg and grounded in Chalmers Computer Engineering, he brings both academic rigor and production-hardened problem solving.
9 years of coding experience
17 years of employment as a software developer
Computer Engineering, Computer Engineering at Chalmers University of Technology
Github Skills (11)
wkt10
javas10
wkb10
geometry10
sql10
java10
spatial-data10
database9
postgresql9
spark9
databases9
Programming languages (4)
Github contributions (5)
apache/sedona
Sep 2021 - Jan 2023
A cluster computing framework for processing large-scale geospatial data
Role in this project:
Back-end Developer & Database Engineer
Back-end Developer & Database EngineerContributions:9 reviews, 16 commits, 21 PRs in 1 year 4 months
Contributions summary:Martin primarily focused on enhancing the functionality and performance of the Apache Sedona project, which is a cluster computing framework for geospatial data processing. Their contributions involved implementing new SQL functions like ST_AsBinary, ST_AsEWKB, ST_SRID, and ST_SetSRID, along with improving existing ones by adding null safety and supporting empty geometries. They also made key optimizations by broadcasting dedupParams to improve performance in join operations and refactored ST_MakeValid to utilize GeometryFixer.
geospatialpythonspatial-analysisspatial-sqlcluster-computing
umartin/sedona
Sep 2021 - May 2023
A cluster computing framework for processing large-scale geospatial data
Contributions:58 pushes, 19 branches in 1 year 8 months
pythongeospatialcluster-computingscalegeospatial-data
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