# Create a Job

> Create a Job activity in the Databricks connector.

## Project compatibility

Windows | Cross-platform

## Overview

| Description | API Method | API Path |
| --- | --- | --- |
| Create a persisted Databricks job. Requires name and tasks; add a schedule for cron-based execution. | POST | `/jobs_create` |

## Input

| Parameter | Description | Data Type |
| --- | --- | --- |
| Job Name | Name of the job. For example, `Daily sales refresh`. | string |
| Tasks \* | Task definitions, for example [{"task_key":"t1","notebook_task":{"notebook_path":"/Users/me/nb"}}]. | array |
| Schedule | Cron schedule: {"quartz_cron_expression":"0 0 9 * * ?","timezone_id":"UTC"}. | object |

\* Required parameter.

## Output

| Parameter | Description | Data Type |
| --- | --- | --- |
| Job ID | ID of the Databricks job. | integer |
| Settings | Full job settings object. | object |
| Created Time | Time the job was created, as a Unix timestamp in milliseconds. | integer |
| Creator | User name of the principal that created the job. | string |

:::note
You might see variations in the output depending on where you run the activity. In API workflows and Maestro Flow, a single response object containing all the fields is returned. In RPA workflows, not all fields are exposed, but you can still retrieve them from the exposed response object.
:::