When you need to find Databricks job status and track down job ownership, navigating a busy workspace can get chaotic fast. Whether a critical pipeline fails in the middle of the night, a task hogs cluster memory, or you’re running a quarterly audit, identifying who built a job and its live execution state shouldn’t take hours.

Tracking down job ownership and live execution status should take a single click. In practice, between traditional Databricks Workflows, Delta Live Tables (DLT), and service accounts, the details get buried fast.

In this guide, you’ll learn how to find Databricks jobs and pipelines, identify the original creator or current owner, and check active execution status using the Databricks UI, Python SDK, and REST API. You’ll also see practical ways to use the Databricks find job status steps in real projects.

Method 1: Databricks Job Creators & Job Status in the Databricks UI

If you need to inspect a few jobs during your shift, the workspace UI provides built-in filters without requiring code.

Step 1: Filter by Owner and Identity to Find Databricks Jobs

Navigate to Workflows (or Jobs & Pipelines) in the left sidebar.

• Filter by Owner: Use the Owner dropdown at the top to isolate jobs created by specific team members or service accounts.
• Filter by Identity: Databricks separates the Creator (who built the job) from the Run As identity (the account or service principal executing it). Filter by Run As when auditing production access and cluster permissions.

Step 2: Check Real-Time Databricks Job Status and Activity

To avoid clicking into individual jobs:

1. Switch to the Runs tab under Workflows.
2. Set the status filter to Running to view all active workspace jobs in real time.
3. Use the matrix view for a quick visual status history of the last 5 runs (Green = Success, Red = Failed, Pink = Skipped/Canceled).

Note on Data Retention: Databricks retains UI job run logs for 60 days. If your team requires long-term compliance or audit logging, export run events to a central Delta Lake table using the REST API or SDK.

Method 2: Programmatically Find Databricks Jobs & Creators via Databricks Python SDK

When managing hundreds of workflows across multiple workspaces, manual UI checks don’t scale. The official databricks-sdk allows you to programmatically list jobs, extract creator metadata, and verify live execution status.

Step 1: Install the SDK to Query Databricks Job Status

Bash

pip install databricks-sdk

Step 2: Python Script to Audit Databricks Job Creators and Run Status

This script iterates through all workspace jobs, retrieves the original author, and prints the latest execution state:

Python

from databricks.sdk import WorkspaceClient
# Automatically uses DATABRICKS_HOST and DATABRICKS_TOKEN environment variables
w = WorkspaceClient()
print(f"{'Job ID':<10} | {'Job Name':<32} | {'Author/Creator':<28} | {'Current Status'}")
print("=" * 85)
for job in w.jobs.list():
    job_id = job.job_id
    name = job.settings.name if job.settings else "Unnamed Job"
    creator = job.creator_user_name or "Unknown / Deactivated User"
    
    # Retrieve the single latest run for the job
    runs = list(w.jobs.list_runs(job_id=job_id, limit=1))
    
    if runs:
        latest = runs[0]
        state = latest.state.life_cycle_state.value
        if latest.state.result_state:
            state += f" ({latest.state.result_state.value})"
    else:
        state = "No recent runs"
    print(f"{job_id:<10} | {name[:30]:<32} | {creator[:26]:<28} | {state}")

Method 3: Finding Delta Live Tables (DLT) Pipelines, Authors & Databricks Pipeline Status

Delta Live Tables (DLT) pipelines use a distinct framework from standard Databricks jobs. If you need to check pipeline creators and status programmatically, use the dedicated Pipeline REST API.

Querying the DLT Endpoint via REST API for Databricks Pipeline Status

Run this curl command to list DLT pipelines in your workspace:

Bash

curl -X GET \
  https://<your-workspace-instance>/api/2.0/pipelines \
  -H "Authorization: Bearer $DATABRICKS_TOKEN"

Understanding the API Response for Databricks Pipeline and Job Status

Look for creator_user_name and state inside the JSON response payload:

JSON

{
  "pipeline_id": "7b89f01a-2345-6789-abcd-ef0123456789",
  "name": "sales_bronze_to_silver",
  "creator_user_name": "data-eng-lead@company.com",
  "state": "RUNNING",
  "health": "HEALTHY"
}

Best Practices for Databricks Job Status Monitoring & Job Management

• Handle Deactivated Accounts: When an engineer leaves the company, creator_user_name their account remains in historical logs. Transfer job permissions to an active user or Service Principal to prevent scheduled runs from failing due to deactivated user credentials.
• Enforce Custom Key-Value Tags: Require team members to apply tags like env: production and department: finance during job creation. Tagging simplifies search and filtering in both the UI and SDK.
• Monitor Active Runs via API: Automate alerts for long-running or stuck jobs by querying active runs (w.jobs.list_runs(active_only=True)on a scheduled monitoring workflow.

Frequently Asked Questions (FAQ) on Finding Databricks Jobs & Job Status

How do I find who created a job and job status in Databricks?

In the UI, open Workflows, click on the job name, and check the Job Details sidebar on the right. Programmatically, inspect the creator_user_name property using the Databricks Python SDK or the /api/2.2/jobs/get REST endpoint.

What is the difference between Job Creator and Run As in Databricks job status?

The Creator is the user account that originally created the job definition. The Run As identity is the user or Service Principal whose permissions are used to execute the job and access underlying data resources.

How long does Databricks keep job run history and job status logs?

Databricks stores job execution logs and run history in the UI for 60 days. For longer retention, stream job run metrics to an external table or cloud storage bucket.