Developing ETL pipelines in Databricks comes with challenges like managing diverse data sources, optimizing Spark performance, and controlling cloud costs. Ensuring data quality, handling errors, and maintaining security and compliance add complexity. Teams also face hurdles with version control, workflow orchestration, and the learning curve of Spark. Balancing scalability, efficiency, and cost remains a key concern throughout the ETL process.