Automated ETL Pipeline Monitoring and Data Quality Scorecarding for Enterprise Data Warehouses

Modern enterprise decision-making relies heavily on automated Extract, Transform, Load (ETL) pipelines feeding massive Enterprise Data Warehouses (EDWs) and Cloud Lakehouses. However, silent data corruption, schema evolution anomalies, missing records, and temporal processing latency frequently degrade data integrity downstream. Traditional rule-based data validation and manual threshold monitoring fail to scale across heterogeneous, high-velocity data environments. Building upon continuous data pipeline governance and monitoring methodologies, this paper presents an end-to-end framework for continuous automated ETL pipeline monitoring and real-time Data Quality (DQ) scorecarding. Leveraging deep multi-variate non-linear anomaly detection models and dynamic weighting vectors across key dimensions—completeness, accuracy, freshness, uniqueness, consistency, and validity—our system generates automated health scorecards prior to data loading into warehouse target tables. Validated on an enterprise EDW dataset comprising 4.8 billion records ingested across 1,250 production pipelines from 2018 to 2022, the proposed architecture reduced undetected downstream data defects by 41.6% and lowered mean time to detection (MTTD) from 14.2 hours to 8.4 minutes. Furthermore, we evaluate structural trade-offs in computational overhead and compute resource allocation under peak batch and streaming workloads.