DATA OPS

Route dbt Run Failures to Downstream Dashboard Owners

When a dbt model fails, it traces every dashboard and table that depends on that model and pings each owner in Slack so the right people hear about broken data before…

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • Triggerdbt run posts failure to webhookHTTP webhook
  • ActionQuery Snowflake lineage for downstream dashboards + ownersSnowflakeSnowflake
  • LogicBranch: owner-tagged vs fall back to on-call
  • ActionDM each dashboard owner the impact in SlackSlack
  • OutputPost team-channel summary of affected assetsSlack

What it does

Turns a raw dbt run failure into targeted, ownership-aware alerts. Instead of dumping a stack trace into one channel, it walks the lineage downstream of the failed model, finds which dashboards and marts are affected, and notifies the specific owner of each one.

When to use it

Use it when your dbt project feeds many BI dashboards and a single failing model silently breaks several of them. Ideal for analytics teams where dashboard ownership is distributed and a generic #data-alerts blast gets ignored.

How it works

  1. 1A dbt run posts its results to a webhook on failure.
  2. 2The flow reads the failed model's `unique_id` and queries Snowflake's information schema plus a lineage/exposures table to list every downstream dashboard and its owner.
  3. 3A branch checks whether any downstream exposure is owner-tagged; untagged ones fall back to the data on-call.
  4. 4For each affected dashboard, a Slack DM goes to its owner with the model name, error, and which of their dashboards is now stale.
  5. 5A summary message lands in the team channel for visibility.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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