Moritz Körber is Head of Device Data Engineering at ZEISS with nine years of experience building data warehouses and lakehouses on AWS and Azure using Python, Spark, SQL and a modern tooling stack including Databricks, Delta Lake, Docker and CI/CD. He combines hands‑on ETL/ELT engineering with strong inferential statistics and predictive modeling skills, ensuring pipelines are production‑grade through unit tests and data quality checks (pytest, Soda). A former data scientist and PhD researcher in human factors from TUM, he brings a researcher's rigor and user-centered perspective to data architecture and validation. Moritz favors infrastructure as code and lightweight development workflows (VS Code), and he balances pragmatic engineering with attention to reproducibility and observability. Based in Munich, he’s known for bridging academic depth with practical, auditable data platforms that reliably power device analytics.
9 years of coding experience
11 years of employment as a software developer
Diplom Psychology, Diplom Psychology at University of Regensburg
Doctor of Philosophy - PhD Human Factors, Doctor of Philosophy - PhD Human Factors at Technical University of Munich
A Covid-19 data pipeline on AWS featuring PySpark/Glue, Docker, Great Expectations, Airflow, and Redshift, templated in CloudFormation and CDK, deployable via Github Actions.
Contributions:115 commits, 97 PRs, 201 pushes in 1 year 3 months
airflowawsdata-pipelinedockerpyspark
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