With regard to point below that has been accepted as a solution
"I will highly recommend to save your data as Delta instead of parquet. There are many extra benefits in Delta"

The fundamental considerations for optimizing write operations, especially those involving shuffling and partitioning, remain similar to Parquet for Delta. When you use partitionedBy during a write operation in Spark, it involves a shuffle operation to redistribute the data across the specified partitions. This is true for both Parquet and Delta tables because they both rely on Spark engine for data processing. My inclination would be to observe from Spark UI (4040), and the staging tab where insert (job) are taking longest . Evaluate the metrics related to tasks, such as input/output data size, shuffle read/write, and CPU time. Both SQL and executors tabs will also help in pinpointing the issue. You can also use compression like SNAPPY to reduce volume of writes.

HTH