Hi @Daniel_dlh ,

you can try to use YAML anchor, have a look at this example:

before:

databricks.yml
variables:
  my_cluster:
    description: "Base cluster configuration"
    default:
      spark_version: "15.4.x-scala2.12"
      node_type_id: "Standard_DS3_v2"
      num_workers: 1

# my_jobs.yml
resources:
  jobs:
    sample_etl_job:
      job_clusters:
        - job_cluster_key: etl_cluster
          new_cluster:
            <<: ${var.my_cluster}  # ERROR: map merge requires map or sequence of maps
            custom_tags:
              environment: ${bundle.target}

with anchor:

# Define base cluster as YAML anchor
definitions:
  base_cluster: &base_cluster
    spark_version: "15.4.x-scala2.12"
    node_type_id: "Standard_DS3_v2"
    num_workers: 1
    spark_conf:
      spark.databricks.cluster.profile: "serverless"
      spark.master: "local[*, 4]"

resources:
  jobs:
    sample_etl_job:
      name: sample_etl_job
      job_clusters:
        - job_cluster_key: etl_cluster
          new_cluster:
            <<: *base_cluster
            custom_tags:
              environment: ${bundle.target}
              project: dlt_telco
      tasks:
        - task_key: etl_task
          job_cluster_key: etl_cluster
          spark_python_task:
            python_file: ../src/sample_etl.py
      schedule:
        quartz_cron_expression: "0 0 1 * * ?"
        timezone_id: "UTC"
      max_concurrent_runs: 1
      timeout_seconds: 3600

 or explicit fields:

# Serverless Jobs Configuration
resources:
  jobs:
    sample_etl_job:
      name: sample_etl_job
      job_clusters:
        - job_cluster_key: etl_cluster
          new_cluster:
            spark_version: ${var.my_cluster.spark_version}
            node_type_id: ${var.my_cluster.node_type_id}
            num_workers: ${var.my_cluster.num_workers}
            spark_conf: ${var.my_cluster.spark_conf}
            custom_tags:
              environment: ${bundle.target}
              project: dlt_telco
      tasks:
        - task_key: etl_task
          job_cluster_key: etl_cluster
          spark_python_task:
            python_file: ../src/sample_etl.py
      schedule:
        quartz_cron_expression: "0 0 1 * * ?"
        timezone_id: "UTC"
      max_concurrent_runs: 1
      timeout_seconds: 3600

The key concept: YAML anchors only work within a single file, so if you want to share an anchor across multiple jobs, you must put the anchor definition AND all the jobs that use it in the SAME file