HR & RECRUITING

Comp-band drift audit from BigQuery to Monday manager review

Nightly compares each employee's pay against refreshed band midpoints in BigQuery, flags anyone outside the band, and opens a Monday review item assigned to their manager.

CategoryHR & Recruiting
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires the audit
  • ActionQuery pay vs refreshed band midpoints in BigQueryGoogle BigQueryBigQuery
  • LogicKeep employees whose pay is outside band min/max
  • ActionCreate Monday review item assigned to managermonday.com
  • OutputPost run summary of items opened by departmentmonday.com

What it does

This workflow runs the core compensation-band drift audit. It pulls current pay and the latest refreshed band midpoints from BigQuery, calculates each employee's position relative to their band, and queues a manager review on Monday for anyone who has drifted below the minimum or above the maximum.

When to use it

Run this after a comp-band refresh (annual or mid-year market adjustment) when you need to systematically catch employees whose salary no longer fits their re-baselined band. Ideal for People Ops teams who manage review cycles in Monday and warehouse HR data in BigQuery.

How it works

  1. 1A nightly schedule fires the audit.
  2. 2A BigQuery query joins the employee roster to the refreshed band table and computes each person's compa-ratio against the new midpoint.
  3. 3A filter keeps only employees whose pay falls outside their band's min/max range.
  4. 4For each flagged employee, a Monday item is created on the review board with the gap amount, suggested midpoint, and manager as owner.
  5. 5The output posts a run summary listing how many items were opened by department.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect monday.comVisual work management for teams.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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