DATA OPS
Dropped-column impact analysis with Linear issue and owner notification
When a Snowflake column is dropped or renamed, traces which downstream tables and dbt models reference it, files a Linear issue scoped to the impact.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled drift scan fires
- ActionDetect dropped or renamed Snowflake columnsSnowflake
- LogicExit if no destructive change
- ActionSearch dbt repo for column referencesGitHub
- ActionFile scoped Linear issue with impact listLinear
- OutputDM affected model owners on SlackSlack
What it does
Goes beyond detecting a dropped column — it figures out the blast radius. On detecting a removed or renamed column, it scans your dbt project in GitHub for references to that column, builds an impact list of affected models and their owners, files a Linear issue with that impact attached, and notifies each owner directly.
When to use it
Use it when a single source-table change can cascade through dozens of dependent models and you need the remediation routed to the right people with the full dependency picture, not just a generic 'schema changed' alert.
How it works
- 1A scheduled trigger fires.
- 2Detect dropped or renamed columns in Snowflake versus the last baseline.
- 3If none, exit; if found, search the dbt repo in GitHub for references to each affected column.
- 4Build the impact list of downstream models and their declared owners.
- 5Create a Linear issue with the column change and the impacted-model list.
- 6DM each owner on Slack with their specific affected models and the Linear link.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Data Ops workflows
Snowflake column type-drift sentinel with Linear fix ticket
Snapshots the data types of every column in your tracked Snowflake schemas on a schedule, diffs against the last snapshot.
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 dropped/renamed column sentinel with PagerDuty incident
Detects when a column is dropped or renamed in your governed BigQuery datasets and, because that breaks downstream queries hard, pages the on-call via PagerDuty and posts…
PR-time Snowflake schema contract check on dbt model changes
When a pull request changes a dbt model, it compares the model's declared output columns against the live Snowflake table it will replace and blocks the merge with a GitHub check…
Agent-triaged warehouse drift with impact analysis and runbook update
On a webhook from your warehouse audit log, an agent investigates the changed column, traces which downstream models and dashboards depend on it.
Cross-warehouse replication schema mismatch reconciler
Compares the column shape of mirrored tables between BigQuery and Snowflake and, when a replicated table has drifted out of sync between the two, opens an Asana task for the data…
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.
