Niclas Jern
SVP, Engineering Data Services
Turku, Mainland Finland
Summary
👤
Senior🎓
Top SchoolNiclas Jern is a seasoned engineering leader and technologist based in Turku, Finland, currently SVP of Engineering & Data Services at Walkbase, where he shapes the technology roadmap and oversees the platform powering large-scale location analytics. He combines hands-on software architecture with strategic leadership, balancing core back-end design for hundreds of millions of events daily with driving hardware and sensor initiatives. As Walkbase co-founder and former VP of Engineering & Co-founder, he built and led teams that bridge in-store analytics, online-offline integration, and partner ecosystems within the STRATACACHE family. His background spans embedded firmware, mobile development, and distributed data processing systems, and he has lectured MSc-level mobile development, reflecting a commitment to mentoring and knowledge-sharing. In addition to his product work, he contributes to open-source back-end projects, notably refactoring and optimizing code in Go-based ML projects, underscoring a focus on code quality and performance. Niclas holds a Master’s in Computer Engineering from Åbo Akademi University and has a track record of turning complex requirements into scalable, auditable solutions.
13 years of coding experience
9 years of employment as a software developer
International Baccalaureate Diploma Programme, International Baccalaureate Diploma Programme at Vasa ÖvningsskolaMaster’s Degree, Computer Engineering, Master’s Degree, Computer Engineering at Åbo Akademi
Swedish, Finnish, English, German
Github Skills (6)
code-optimization10
machine-learning10
refactoring10
go10
refactorings10
algorithms8
Programming languages (6)
Github contributions (5)
sjwhitworth/golearn
Jul 2014 - Jul 2014
Machine Learning for Go
Role in this project:
Back-end Developer
Back-end DeveloperContributions:23 commits, 3 comments, 3 issues in 2 days
Contributions summary:Niclas primarily focused on refactoring and optimizing the existing Go code base. Their contributions include simplifying range statements, streamlining conditional logic, and replacing verbose increment operations. They also made improvements to several key files, including utilities, bagging, and linear models, suggesting involvement in core logic refinement and performance improvements. This indicates a focus on code quality and efficiency within the machine learning project.
golangmachine-learningdata-science
njern/gonexmo
Dec 2013 - Aug 2018
A Nexmo library for Golang
Contributions:1 release, 28 commits, 9 PRs in 4 years 8 months
golangnexmogogo-modules
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