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.

Key Takeaways:
1. Innovative Use of GPT-4: Leveraging large language models like GPT-4 can revolutionize traditional data engineering tasks, offering autonomous solutions.
2. Improved Anomaly Detection: By analyzing historical data, our system provides optimized thresholds for anomaly detectors, balancing alert sensitivity and accuracy.
3. Efficient Issue Resolution: The approach significantly reduces resolution times, allowing engineers to focus on intricate challenges.
4. Empirical Validation: Our case studies validate improved metrics and overall system stability, suggesting tangible benefits of integrating AI in on-call data engineering.