Christian Edelmayer
Data Engineer
Vienna, Austria
Summary
🤩
Rockstar🎓
Top SchoolChristian Edelmayer is a data engineer with 7 years of experience turning messy datasets into production-ready pipelines and ML-ready platforms, currently building cloud-native data solutions at paiqo in Vienna. He combines a strong computer science background and practical systems programming chops—evident from contributions to a self-hosting RISC-V compiler/emulator project—with hands-on expertise in Azure, Hadoop, SQL/NoSQL, Python, R and Power BI. As a founder of a boutique data consultancy he has led end-to-end projects from exploration to deployment, often in supply chain contexts integrating SAP and streaming sources. He is comfortable bridging data science and engineering: designing architectures, ensuring data quality, and preparing features for ML use cases. Colleagues value his mathematical mindset and propensity for outside-the-box technical solutions that surface actionable business insights.
7 years of coding experience
2 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Paris Lodron Universität Salzburg
German, English
Github Skills (11)
c1710
risc-v10
compiler10
compiler-compiler10
virtual-machine10
c1110
emulation10
algorithms8
data-structures8
algorithm8
data-structure8
Programming languages (5)
Github contributions (5)
cksystemsteaching/selfie
Apr 2019 - Apr 2020
An educational software system of a tiny self-compiling C compiler, a tiny self-executing RISC-V emulator, and a tiny self-hosting RISC-V hypervisor.
Role in this project:
Back-end Developer & Systems Engineer
Back-end Developer & Systems EngineerContributions:2 releases, 71 commits, 7 PRs in 1 year
Contributions summary:Christian primarily contributed to the development of the `selfie` compiler and emulator. Their work focused on creating and refining symbolic execution examples, including nested loops, conditional statements, and recursion. The user also implemented core features like the "follow and merge decision" mechanism, division-by-zero checks, and invalid memory access checks within the interpreter, demonstrating systems-level programming skills. Furthermore, the user refined the handling of recursion and integrated more advanced examples.
emulatorsymbolic-execution-enginecompilercomputer-sciencecompiling
ChrisEdel/vehicle-classification
Aug 2021 - Jan 2022
The aim of this project is to use the LiDAR sensor of the new Apple devices in combination with the camera sensors in order to classify cars (based on the make and model) using deep learning. This repository contains the corresponding code consisting of the iOS App, a preprocessing and augmentation script, and our custom neural network.
Contributions:3 reviews, 32 commits, 2 PRs in 6 months
aimsensorscameracarssensor
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