troubleshooting-dbt-job-errors

Diagnose dbt Cloud job failures by analyzing run logs and querying the MCP Admin API to classify errors as infrastructure, code, or data issues and identify root causes. The skill uses list_jobs_runs and get_job_run_error tools to gather run data.

dbt Cloud jobs fail with unclear errors due to infrastructure issues, code compilation failures, or data and test constraint violations. The skill systematically reviews run_results.json, git history, and Admin API data to pinpoint the exact failure category.

Troubleshoot intermittent dbt platform job issues by querying the MCP Admin API for recent job runs and reviewing historical logs. This systematic approach identifies whether root causes stem from infrastructure, code compilation, or data test failures.

MCP Admin API access is required to gather job run information and error details. You also need the failed job run_id, available run logs, and run_results.json to properly diagnose and resolve dbt Cloud job failures.

This approach is not intended for local dbt development errors. It specifically diagnoses and resolves dbt Cloud and platform job failures occurring in CI/CD pipelines or cloud environments where MCP Admin API data and run logs are available.

Fix dbt job failures with unidentified root causes by following actionable remediation guidance that includes creating branches, applying fixes, and documenting the issue. The skill classifies errors to narrow down whether the problem is infrastructure, code, or data related.