Datawarehouse ontwikkelaar

Research and prototype an AI-assisted approach to audit enterprise data migrations, improving accuracy, traceability, and efficiency of validation across source and target systems. Core Responsibilities - Define audit objectives, quality metrics, and acceptance criteria for migrated data (completeness, consistency, accuracy, timeliness). - Design a methodology to detect anomalies, mismatches, and rule violations using AI/ML and statistical techniques. - Build a proof-of-concept pipeline for automated checks, reconciliation, and reporting. - Develop explainable audit outputs (evidence, confidence scores, root-cause hints) for stakeholders. - Evaluate performance using benchmark datasets and real-world migration scenarios; document findings and limitations. Required Skills - Data engineering: ETL/ELT concepts, data profiling, reconciliation, and data quality frameworks. - AI/ML: anomaly detection, classification, clustering, LLM-assisted rule extraction or test generation. -

Programming: Python (preferred) and SQL; experience with notebooks and reproducible experiments. - Data platforms: relational databases and/or cloud data warehouses; understanding of schemas and metadata. - Communication: ability to translate technical results into clear audit reports. Expected Outcomes - A documented audit framework and evaluation results. - A working prototype demonstrating AI-powered migration auditing and reporting. Large-scale data migrations at NN must not only transform and clean massive datasets but also prove, with full auditability, that the numbers still add up. Manual reconciliation is slow, error-prone, and costly, especially under the scrutiny of DNB audits. This thesis tackles that challenge by designing an AI-supported framework that automatically verifies numeric and provenance consistency across medallion...