What causes DbtCloudJobRunException?

The Airflow task's execution_timeout is shorter than the typical dbt Cloud job run duration. A dbt model or test ran unexpectedly slowly due to missing incremental filters or warehouse scaling issues. The dbt Cloud job timeout (set in the job definition) is too aggressive for the data volume. Network latency between the Airflow worker and the dbt Cloud API caused polling delays that consumed the timeout budget

How do I fix DbtCloudJobRunException?

Step 1: Check the dbt Cloud run logs to see which model or test was running when the timeout fired.. Step 2: Increase execution_timeout in the Airflow operator definition to give the job adequate headroom.. Step 3: In the dbt Cloud job settings, review the Timeout Seconds value and increase it if needed.. Step 4: Optimise the slow model — add incremental materialisation or partition filters to reduce run time.. Step 5: Increase the check_interval on the operator to reduce API polling overhead if rate limits are a factor.

High severitytimeout

Power BI Refresh Error:
DbtCloudJobRunException

What does this error mean?

The Airflow DbtCloudRunJobOperator cancelled a dbt Cloud job because the run exceeded the operator's execution_timeout or the job's own timeout configuration.

Common causes

1The Airflow task's execution_timeout is shorter than the typical dbt Cloud job run duration
2A dbt model or test ran unexpectedly slowly due to missing incremental filters or warehouse scaling issues
3The dbt Cloud job timeout (set in the job definition) is too aggressive for the data volume
4Network latency between the Airflow worker and the dbt Cloud API caused polling delays that consumed the timeout budget

How to fix it

1Step 1: Check the dbt Cloud run logs to see which model or test was running when the timeout fired.
2Step 2: Increase execution_timeout in the Airflow operator definition to give the job adequate headroom.
3Step 3: In the dbt Cloud job settings, review the Timeout Seconds value and increase it if needed.
4Step 4: Optimise the slow model — add incremental materialisation or partition filters to reduce run time.
5Step 5: Increase the check_interval on the operator to reduce API polling overhead if rate limits are a factor.

Frequently asked questions

Does cancelling the Airflow task also cancel the dbt Cloud run?

Yes — the DbtCloudRunJobOperator sends a cancel request to the dbt Cloud API when the Airflow task times out or is killed, provided the operator version supports graceful shutdown. Verify this in your provider version's changelog.

What is a safe execution_timeout value to set?

Set execution_timeout to 2–3x the p95 run duration of the dbt job. Review the dbt Cloud job run history for the past 30 days to establish a reliable baseline before choosing the timeout value.