I have a scenario where my Databricks asset bundles require two types of job parameters:

• Common parameters that apply to all environments
• Environment-specific parameters that differ per environment.

My current YAML setup is structured like this:

# common_parameters.yaml
common_parameters: &common_parameters
  - name: job_var
    default: "common job var value"
  - name: retry_count
    default: 3 # example common param
  - name: timeout_seconds
    default: 600 # example common param

# env_params.yaml
dev_parameters: &dev_parameters
  - name: redshift_host
    default: ${var.redshift_host}
  - name: test_param_dev # new param for testing in dev
    default: "test value dev"

prod_parameters: &prod_parameters
  - name: redshift_host
    default: ${var.redshift_host}
  - name: test_param_prod # new param for testing in prod
    default: "test value prod"

# notifications.yaml
dev_notifications: &dev_notifications
  email_notifications:
    on_failure:
      - "dev-team@example.com"

prod_notifications: &prod_notifications
  email_notifications:
    on_failure:
      - "prod-alerts@example.com"

# schedules.yaml
prod_schedule: &prod_schedule
  schedule:
    quartz_cron_expression: "0 0 0 * * ?"
    timezone_id: "UTC"
    pause_status: "PAUSED"

# main job definition and targets
resources:
  jobs:
    dummy_job:
      name: "dummy_workflow"
      edit_mode: "EDITABLE"
      tasks:
        - task_key: ingestion
          notebook_task:
            notebook_path: ../src/ingestion/test.py
            source: "WORKSPACE"

targets:
  dev:
    resources:
      jobs:
        dummy_job:
          parameters: *dev_parameters

  prod:
    resources:
      jobs:
        dummy_job:
          parameters: *prod_parameters

How do I include or merge the common parameters with the environment-specific parameters within the parameters list and is there any best practices to manage this kind of parameter reuse in Databricks asset bundle YAMLs?