DATA OPS
Snowflake Schema-Drift Sentinel to ClickUp Fix Task
Runs nightly, diffs live Snowflake column types and nullability against your dbt model contract.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule before dbt build
- ActionQuery Snowflake INFORMATION_SCHEMA for live column shapesSnowflake
- ActionLoad dbt contract definitions from repoGitHub
- LogicDiff live columns vs. contract; flag adds/drops/retypes
- LogicExit quietly if no drift detected
- OutputOpen ClickUp fix task with column deltas per modelClickUp
What it does
This workflow catches silent schema drift in Snowflake before it breaks dbt builds or dashboards. Each night it pulls the actual column set for your contracted models from `INFORMATION_SCHEMA`, compares it to the column names, data types, and nullability declared in your dbt contract, and files a single ClickUp task enumerating every mismatch. Clean runs produce nothing, so the only signal an operator ever sees is real drift.
When to use it
Use it when upstream teams own the tables your dbt models read and can change columns without telling you. It is the right fit for warehouses where a renamed or retyped column would otherwise surface as a failed dbt run hours later, or as a wrong number on a board.
How it works
- 1A nightly schedule fires before the dbt build window.
- 2Query Snowflake `INFORMATION_SCHEMA.COLUMNS` for every contracted model's live shape.
- 3Load the committed dbt contract definitions from the repo.
- 4Diff live vs. contract: flag added, dropped, retyped, and nullability-changed columns.
- 5If the diff is empty, exit quietly.
- 6Open a ClickUp task per affected model with the exact column deltas and the owning team assigned.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect ClickUpDocs + tasks + chats in one workspace.
- 3Connect GitHubRepos, issues, pull requests, actions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Data Ops workflows
Weekly BigQuery Cost Trend Sheet and Exec Digest
Compiles week-over-week BigQuery scheduled-query cost by owner and dataset into a Google Sheet with trend columns.
Daily BigQuery Scheduled-Query Cost Attribution to Owners
Each morning, totals the prior day's on-demand bytes-billed per scheduled query, maps each query to its owner from a label, and posts a per-owner cost leaderboard to Slack.
BigQuery Per-Team Budget Breach Alert to PagerDuty
Tracks month-to-date BigQuery scheduled-query spend per team and, when a team crosses its monthly budget, pages the team's on-call in PagerDuty and snapshots the spend breakdown…
dbt source freshness watcher with severity-routed alerts
Checks Snowflake loaded-at timestamps against each dbt source's freshness SLA, then routes warnings to Slack and hard breaches to a PagerDuty incident so stale data never…
dbt orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
Raw Sensor Telemetry Archive to BigQuery
Captures every incoming building sensor reading via webhook, normalizes the payload into a consistent schema.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.

Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.
