Developing ETL pipelines using Databricks can present several key challenges. First, managing large volumes of data efficiently can be tricky, especially when dealing with different data sources and formats. Second, ensuring scalability and performance optimization is crucial, particularly for handling complex transformations. Third, troubleshooting and debugging can be difficult, as Databricks is highly distributed and errors may not always be straightforward. Finally, integrating Databricks with existing data infrastructure and maintaining data quality throughout the pipeline requires careful planning and continuous monitoring.