DATA OPS
Monthly warehouse chargeback rollup to Airtable
At month-end, sums each team's BigQuery spend by mapping query authors to their team, writes a chargeback row per team into Airtable.
How it runs
The automated pipeline, trigger to output.
- TriggerMonthly first-of-month schedule
- ActionAggregate prior-month cost per author in BigQueryBigQuery
- ActionRead author-to-team mapping from AirtableAirtable
- LogicJoin authors to teams and roll cost up per team
- ActionUpsert per-team chargeback row in AirtableAirtable
- OutputPost chargeback summary to finance SlackSlack
What it does
Aggregates the full month of BigQuery job cost per author, joins authors to their team using an Airtable lookup table, rolls cost up to the team level, and upserts one chargeback record per team into an Airtable base. It then posts a summary to the finance Slack channel with the month's total and the biggest team.
When to use it
Use this when finance does internal cost allocation and you are tired of rebuilding the warehouse-spend spreadsheet every month. It turns raw query logs into a clean, per-team chargeback table that finance can reconcile directly.
How it works
- 1A scheduled trigger fires on the first of each month for the prior month.
- 2A BigQuery action aggregates `INFORMATION_SCHEMA.JOBS` cost grouped by `user_email`.
- 3An Airtable action reads the author-to-team mapping table.
- 4A logic step joins authors to teams and rolls cost up per team, flagging any unmapped authors.
- 5An Airtable action upserts a chargeback row per team for the month.
- 6A Slack action posts the chargeback summary to finance.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect AirtableBases, tables, views, automations.
- 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.
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