Data engineering teams encounter challenges like data quality issues and pipeline failures, especially in enterprise environments. Addressing this, our approach combines the linguistic prowess of models like GPT-4 with data engineering tasks. We autonomously identify and rectify data quality issues, transform anomaly detection paradigms, and automate recovery tasks. Our methodology achieves reduced resolution times, fine-tuned anomaly detectors, and minimized downtime. Empirical evidence showcases enhanced metrics such as reduced MTTR and fewer false positives, advocating a future where AI plays a pivotal role in on-call data engineering.