Mārtiņš Kalvāns
Consultant And Contractor at EMKIT SIA
Latvia
Summary
🤩
Rockstar🎓
Top SchoolMārtiņš Kalvāns is a seasoned BigData, backend and ML engineer with 12 years of experience building scalable data platforms and software infrastructure from on-prem to GCP. He spent five years shaping data quality and management at Spotify, led Cost Insights product work to make engineers cost-aware, and more recently consults and contracts on large-scale data processing and backend systems. An active open-source contributor, he has improved core functionality in high-profile projects like Spotify’s Scio (Apache Beam API) and Luigi, focusing on serialization, robust Bigtable writes and resilient remote filesystem transfers. He also teaches internal BigData and ML courses, blending hands-on engineering with developer enablement and platform UX improvements. Based in Latvia with a master’s in computer science, he brings a pragmatic focus on reliability, developer experience and cost-conscious optimizations across data ecosystems.
12 years of coding experience
22 years of employment as a software developer
Masters Computer Science, Masters Computer Science at Latvijas UniversitateSchool -, School - at Riga State Gymnasium No. 1
Stackoverflow
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Github Skills (22)
python10
data-engineering10
luigi10
java10
scala10
javas10
orchestration-framework10
apache-beam10
ssh10
hadoop9
testing9
serialization9
bigtable9
data-serialization9
scheduling9
Programming languages (6)
Github contributions (5)
spotify/luigi
Sep 2013 - Aug 2021
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 1 PR, 29 comments in 8 years
Contributions summary:Mārtiņš primarily contributed to the `luigi` Python module, focusing on enhancements to its remote file system capabilities. Their work involved adding features like writing to remote targets with atomicity and implementing functions for `put` and `get` operations, enabling data transfer and management on remote systems. The user also refactored existing code, improved the efficiency of the file system interactions, and added compression to remote file transfers. Furthermore, they fixed a partial read issue within the `luigi` library.
pythonbatch-jobsdependency-resolutionorchestration-frameworkworkflow-management
spotify/scio
Jun 2017 - Aug 2019
A Scala API for Apache Beam and Google Cloud Dataflow.
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits, 6 PRs, 37 comments in 2 years 1 month
Contributions summary:Mārtiņš contributed to the Scio project by implementing fixes and improvements to several core functionalities. This includes addressing Bigtable write failures, removing a `sys.exit` call in a test-related code path, and adding a WrappedArray coder to handle serialization. They also addressed a bug in the version check, and improved the Java Instant coder to fully support the instant and nanosecond range. These changes reflect a focus on enhancing data processing capabilities, data serialization, and improving overall software reliability.
apidatabeambatchbigquery
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