To optimize data pipeline development on Databricks for large-scale workloads, focus on efficient data processing and resource management. Leverage Apache Spark's distributed computing capabilities to handle massive datasets. Use Delta Lake for reliable, ACID-compliant storage and faster query performance. Implement partitioning, caching, and parallel processing to improve speed and reduce latency. Automate scaling using Databricks' autoscaling clusters and optimize ETL jobs with optimized Spark configurations. Monitoring and fine-tuning resource usage further enhance pipeline efficiency and minimize costs.