Generative AI is significantly impacting the work of data engineers, with its potential to create synthetic data or augment existing data offering additional resources for analysis and management. This increased volume of data can provide more comprehensive analyses and testing without relying solely on actual data, improving the robustness and utility of data-driven models and systems. However, it also presents challenges in terms of data observability, as the newly created data introduces new complexities and nuances that need to be thoroughly understood and managed. AI can enhance data engineering by improving data discovery and access, facilitating data integration and interoperability, automating data analytics tools, and contributing to data democratization. Data observability ensures the reliability, quality, and accuracy of generated data, allowing for real-time monitoring and analysis of this data.