DATA OPS
Weekly reverse-ETL freshness scorecard to Notion
Each week, an agent profiles every reverse-ETL synced object, scores its freshness and drift against the Snowflake source.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule before the data-ops review
- ActionQuery source-of-truth values per object from SnowflakeSnowflake
- LogicScore freshness and drift per object and rank offenders
- ActionWrite the ranked freshness scorecard to NotionNotion
- OutputPost a linked summary to the data-ops Slack channelSlack
What it does
Daily alerts catch fires; this workflow builds the weekly narrative. An agent walks each object your reverse-ETL pipeline syncs (accounts, contacts, opportunities), samples the synced CRM values against the Snowflake source of truth, and computes per-object freshness and drift scores. It then writes a structured Notion scorecard that ranks objects from healthiest to worst, calls out the fields most prone to drift, and recommends where to focus sync fixes. A short Slack summary links the page so the data-ops review meeting starts from evidence rather than anecdote.
When to use it
Use it for recurring data-quality reviews where you want trends and prioritization, not raw row-level alerts. It complements real-time monitors by giving leadership a readable weekly health view.
How it works
- 1A weekly schedule triggers the agent ahead of the review meeting.
- 2The agent enumerates synced objects and queries source values from Snowflake.
- 3It samples the synced values across CRM objects for comparison.
- 4It scores freshness and drift per object and ranks the offenders.
- 5It writes the ranked scorecard as a Notion page.
- 6It posts a linked summary to the data-ops Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect NotionPages, databases, comments.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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
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 orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
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.
Backfill Missing Owner Labels on BigQuery Scheduled Queries
Finds scheduled queries with no owner label, infers the likely owner from creator metadata and target-table lineage, proposes a label.
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.
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…
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.
