Alexander Geiger
Team Lead Data Analytics & Governance
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolAlexander Geiger is a Team Lead for Data Analytics & Governance based in Munich with 8+ years of experience turning complex, regulated enterprise data into high-quality, AI-ready assets. He builds semantic, metadata-driven data layers and Snowflake-based data products that bridge business, BI and engineering, enabling self-service analytics and “Talk-to-Your-Data” AI use cases. His background in credit risk model validation and work with SAP-like relational business logic gives him a strong sense for auditability and regulatory compliance in data pipelines. He led cross-system transformation programs and introduced a data catalog (Atlan) to provide metadata context for AI agents—improving discoverability and governance. On the engineering side he has contributed to open-source time-series anomaly detection tooling, enhancing backend data workflows and storage integrations. He combines product sensibilities from earlier product-owner roles with deep technical fluency in modern cloud data ecosystems (Snowflake, AWS, dbt).
7 years of coding experience
3 years of employment as a software developer
Master of Science (MS), Finance, General, Master of Science (MS), Finance, General at Queen's University BelfastPhysics, Physics at Umeå UniversityBachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at Justus Liebig University Giessen
German, English, Russian
Github Skills (15)
machine-learning10
mongodb10
python10
mongodb-database10
data-engineering10
data-science9
gridfs9
anomaly-detection9
pandas9
unsupervised-learning8
pytest8
time-series7
deep-learning7
docker4
dockers4
Programming languages (1)
Github contributions (4)
sintel-dev/Orion
May 2019 - Feb 2020
Unsupervised time series anomaly detection library
Role in this project:
Backend & Data Engineer
Backend & Data EngineerContributions:16 commits, 2 PRs, 7 pushes in 9 months
Contributions summary:Alexander primarily focused on modifying the backend infrastructure of the "orion" library. They implemented changes to the database schema using MongoDB and integrated GridFS for storing intermediate outputs. Furthermore, they updated dependencies, resolved pytest issues, and improved the experiment running workflow. They also made modifications to the core codebase related to data loading and processing within the anomaly detection pipeline.
benchmarkingdata-sciencedeep-learningunsupervised-learninganomaly-detection
AlexanderGeiger/MLPrimitives
Apr 2019 - Jan 2020
Machine Learning Primitives for MLBlocks
Contributions:2 PRs, 32 pushes, 15 branches in 9 months
primitivesmachine-learningdata-science
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