dbt semantic layer dashboard query failed: MetricFlow error metric not found in semantic model for Tableau sync

TL;DR

Look the object up by listing the current workspace rather than trusting the stored id or name, remap the reference to the current id, and fail loudly when a listing has no match. Start in Tableau Server or Cloud site admin, the extract refresh history, and the workbook page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

dbt semantic layer dashboard query failed: MetricFlow error metric not found in semantic model for Tableau sync

Use this when

• You see this exact message or symptom in Tableau: dbt semantic layer dashboard query failed: MetricFlow error metric not found in semantic model for Tableau sync
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Tableau fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in Tableau Server or Cloud site admin, the extract refresh history, and the workbook page and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the workbook data source, the extract schedule, the Hyper file path, and the site content URL used at sign in, and check the current listing of the named objects, the old versus new id or name, and the remapped reference in the config. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: look the object up by listing the current workspace rather than trusting the stored id or name, remap the reference to the current id, and fail loudly when a listing has no match. Verify: the corrected value or setting is visible in Tableau itself, not only in a local config file
4. Re-run the original operation on the single affected item first: run the extract refresh manually for one data source, watch the data engine job, then open the dashboard. Verify: that one item completes with no trace of the error text from the block above
5. Check the downstream result a user or the next job would see: the refresh completing in the job history and the dashboard showing the new extract timestamp. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
6. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

dbt semantic layer dashboard query failed: MetricFlow error metric not found in semantic model for Tableau sync

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Tableau version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does dbt semantic layer dashboard query failed: MetricFlow error metric not found in semantic model for Tableau sync keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Tableau Server or Cloud 2024 releases or later, Hyper extracts, Tableau REST API for workbook publishing. The steps assume you can read the job or build log and change the named object in Tableau.

Why it happens

In Tableau, this failure usually means the reference the job uses points at an object that was renamed, merged, moved, or never created in this workspace, so the lookup returns nothing. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_nYY3pUIQkCbO2RkIys39tw