DATA OPS

Daily freshness watchdog for Snowflake to Salesforce audience syncs

Each morning, checks the max updated-at timestamp in the Snowflake source model against the last synced timestamp in Salesforce and pages on-call if the audience is stale.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily morning schedule
  • ActionRead max updated-at timestamp from Snowflake modelSnowflakeSnowflake
  • ActionRead latest sync timestamp from Salesforce objectSalesforce
  • LogicCompute freshness lag vs SLA window
  • OutputPage on-call via PagerDuty if audience is stalePagerDutyPagerDuty

What it does

Guards against a frozen reverse-ETL pipeline. It reads the freshest record timestamp in your Snowflake audience model and the most recent sync timestamp on Salesforce records, then measures how far behind the destination has fallen. If the lag breaches your SLA, it escalates.

When to use it

Use it when sales or RevOps acts on data that must be current, like account health scores or intent signals. Reverse-ETL jobs can succeed at zero rows or quietly stop scheduling, leaving Salesforce showing yesterday's truth while everyone assumes it is live. This watchdog turns that invisible staleness into a paged incident.

How it works

  1. 1A daily schedule fires before the sales team starts its day.
  2. 2Query Snowflake for the maximum updated-at timestamp across the audience model.
  3. 3Query Salesforce for the latest sync timestamp on records in the target object.
  4. 4A logic step computes the freshness lag in hours and tests it against the SLA window.
  5. 5If fresh, the run ends quietly; if stale, open a PagerDuty incident with the lag, both timestamps, and the model name so on-call can act immediately.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect SalesforceAccounts, opportunities, cases.
  3. 3
    Connect PagerDutyIncidents, on-call, escalations.
  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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