Hands-on experience with data platforms, ETL/ELT pipelines, and data lake architectures * Semantic and metadata systems: Experience with data catalogs, ontology mapping, knowledge graphs, or metadata-driven architectures We believe the real value of modern enterprises lies in their data. Most large organizations already have the data they need. The data still runs the business, but it can't easily be trusted, reused, or reasoned over consistently by modern software and AI. SAP sits at the center of this problem. Yet while the data is reliable by design, its business meaning is often implicit and difficult for AI systems to interpret, which prevents AI from reasoning over it reliably, safely, and at scale. We know why AI systems struggle to interpret enterprise data correctly, that many existing semantic layers stop at technical abstraction, and why business meaning must be made ...
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