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DATA OPS

Reverse-ETL Field-Level Drift Reconciler

Goes beyond row counts to compare specific field values between warehouse rows and their landed Salesforce records.

CategoryData Ops
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule fires after sync completes
  • ActionPull source rows with audited fields from SnowflakeSnowflakeSnowflake
  • ActionFetch matching Salesforce records and valuesSalesforce
  • LogicCompare fields to find value mismatches
  • ActionWrite per-field drift report to Google SheetGoogle DriveGoogle Drive
  • OutputPost mismatch summary to SlackSlack

What it does

Reconciles content, not just presence. Many reverse-ETL syncs create the destination record but silently drop or stale individual fields because of type coercion, picklist mismatches, or partial upserts. This workflow compares chosen fields (for example, lifecycle stage, owner, ARR) between the Snowflake source row and the matching Salesforce record, then reports every record where a field landed wrong.

When to use it

Use it when row-count reconciliation already passes but you still see Salesforce values that disagree with the warehouse. It is the right tool when correctness of specific fields, not merely record existence, drives reporting or routing decisions downstream.

How it works

  1. 1A schedule fires after the sync completes.
  2. 2Pull source rows with the audited fields from Snowflake.
  3. 3Fetch the matching Salesforce records and their current field values.
  4. 4Compare field by field to find value-level mismatches.
  5. 5Write a per-field drift report to a Google Sheet for the data team to triage.
  6. 6Post a short summary with the mismatch count and top offending fields to Slack.

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 Google DriveDocs, sheets, slides, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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