Data engineers face several challenges in managing customer data, including scalability issues with manual data collection, the proliferation of data silos, the complexity of maintaining custom ETL pipelines, and the burden of SQL queries for data analysis. Manual processes can lead to human error and corrupted data, while data silos create misalignment and hinder collaboration. Custom ETL pipelines, crucial for integrating diverse data sources, are often difficult to maintain, especially as source data changes. Additionally, data engineers frequently handle SQL queries, which can become bottlenecked, limiting the effective use of their technical skills. However, new solutions like Heap offer a promising alternative by automating data capture and simplifying the transformation process, thus alleviating many of these challenges and allowing data engineers to focus on more strategic tasks. Heap's ability to automatically capture events and integrate them with data warehouses like Snowflake is seen as an "easy button" by industry experts, streamlining workflows and reducing maintenance burdens.