AI-Augmented Data Engineering: Enhancing ETL Pipelines with Intelligent Automation and Data Quality Frameworks

Abstract

The exponential growth of enterprise data has exposed critical limitations in conventional Extract, Transform, and Load (ETL) pipelines, which rely heavily on manual intervention, rigid rule-based logic, and reactive error handling. This paper investigates the integration of artificial intelligence and machine learning techniques into modern data engineering workflows to create adaptive, self-optimizing ETL systems capable of addressing these shortcomings. We propose an AI-augmented data engineering framework that embeds intelligent automation across the three core pipeline stages — extraction, transformation, and loading — while simultaneously enforcing robust data quality governance. The framework leverages large language models for schema mapping and anomaly detection, reinforcement learning for dynamic pipeline optimization, and statistical profiling for proactive data validation. Experimental evaluations conducted across heterogeneous data environments demonstrate significant improvements in pipeline throughput, error detection accuracy, and operational efficiency compared to traditional approaches. Furthermore, the proposed quality framework introduces a multi-dimensional scoring mechanism that continuously monitors data completeness, consistency, timeliness, and accuracy in real time. The findings suggest that AI-augmented ETL pipelines not only reduce human overhead but also enhance organizational data reliability, enabling more trustworthy downstream analytics and decision-making processes.