Daniel Bartling
Lead Data Engineer Solution Owner
Stuttgart, Baden-Württemberg, Germany
Summary
🤩
Rockstar🎓
Top SchoolDaniel Bartling is a Lead Data Engineer and Solution Owner at Mercedes-Benz with eight years of experience translating complex risk and time-series problems into production-ready data platforms. He progressed from quantitative roles in corporate controlling and portfolio risk to senior data science and engineering leadership, blending economic rigor from a Diplom in Quantitative Economics with hands-on ML engineering. An active contributor to the sktime project, he has improved core forecasting components and implemented feature engineering tools like calendar dummy extractors and window summarizers, reflecting deep expertise in time-series transformations. Known for making hierarchical and recursive forecasting pipelines more robust, he brings a pragmatic focus on reliable, test-covered solutions that scale in enterprise environments.
8 years of coding experience
7 years of employment as a software developer
Diplom, Quantitative Volkswirtschaftslehre, sehr gut, Diplom, Quantitative Volkswirtschaftslehre, sehr gut at Eberhard Karls Universität Tübingen
Github Skills (13)
scikit10
scikit-learn10
forecasting10
pandas10
machine-learning10
forecast10
time-series10
feature-engineering10
python10
data-science10
classification10
classify10
data-engineering9
Programming languages (9)
Github contributions (5)
sktime/sktime
Oct 2021 - Jan 2023
A unified framework for machine learning with time series
Role in this project:
ML Engineer
ML EngineerContributions:23 reviews, 20 commits, 41 PRs in 1 year 3 months
Contributions summary:Daniel significantly contributed to the sktime library, focusing on enhancing and extending its time series functionality. They implemented a calendar dummy extractor and a window summarizer, demonstrating expertise in feature engineering and time series transformations. The user also addressed bugs and improved the handling of hierarchical data within the framework, leading to more robust and efficient computations. Their work involved modifications to core forecasting components, including the recursive strategy and related tests.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
dbart79/valtransfer
Jul 2021 - Jul 2021
Contributions:48 pushes, 1 branch in 5 days
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