Georg Kraus

Data engineering is only valuable when it enables what comes next — and for me, that means AI-ready, production-grade platforms that don't just move data, but make it actionable in real time. My foundation is deep: 20+ years of database development on-premise with Oracle, PostgreSQL, and SQL Server, covering everything from schema design and query optimization to large-scale data modeling. That experience informs how I approach every modern data platform — with an understanding of what happens when data architecture decisions go wrong at scale. Over the last 6+ years I've moved that expertise into the cloud and the modern data stack, building end-to-end platforms on Azure: streaming architectures with Kafka and Strimzi on Kubernetes, large-scale Spark processing, Databricks lakehouses with Unity Catalog governance, and CI/CD pipelines through Azure DevOps. I work across the medallion architecture — from raw ingestion to curated, ML-ready datasets — with a strong focus on reliability, scalability, and operational maturity. What sets my work apart is the intersection of data engineering and machine learning. Real-time streaming as an input to ML systems, MLOps tooling, and GenAI pipeline design are where I'm investing my energy — because the next generation of AI systems will live and die by the quality of the data infrastructure underneath them. I hold the Databricks Certified Data Engineer Associate certification and actively follow developments across the modern data stack and ML engineering space. Open to connecting with engineers, architects, and teams building serious data and AI infrastructure.