DATA OPS
Agent-assisted BigQuery export that clarifies intent before masked delivery
An agent reads a plain-English export request from a webhook, builds and confirms the BigQuery query, masks PII, and delivers the file to Dropbox after Slack sign-off.
How it runs
The automated pipeline, trigger to output.
- TriggerFree-text export request via webhookHTTP webhook
- LogicAgent drafts query + masking plan, confirms in SlackSlack
- ActionRun confirmed query in BigQueryBigQuery
- LogicApply confirmed PII masking
- ActionUpload masked file to DropboxDropbox
- OutputConfirm delivery with link and query in SlackSlack
What it does
Handles fuzzy, plain-English export requests. An agent interprets the request, drafts the BigQuery query and the masking plan, and posts both to Slack for the requester to confirm the intent is right. Once confirmed, it runs the query, masks PII, and delivers the file to Dropbox. This catches misunderstood requests before any data is pulled.
When to use it
Use it when export requests arrive as free-form text rather than structured forms and you want an agent to translate intent into a precise, reviewable query instead of guessing. The confirmation step prevents wrong-data deliveries.
How it works
- 1A webhook receives the free-text export request.
- 2The agent drafts a BigQuery query and a proposed masking plan, posting both to Slack for confirmation.
- 3On confirmation, BigQuery runs the agreed query.
- 4A masking step applies the confirmed PII redactions.
- 5The masked file is uploaded to Dropbox.
- 6Slack confirms delivery with the link and the query that was run.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect DropboxFiles and folders.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Connect HTTP webhookTrigger any URL on agent actions.
- 5Connect OpenAIModels, embeddings, files.
- 6Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 7Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 8Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Data Ops workflows
Snowflake column type-drift sentinel with Linear fix ticket
Snapshots the data types of every column in your tracked Snowflake schemas on a schedule, diffs against the last snapshot.
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.
BigQuery dropped/renamed column sentinel with PagerDuty incident
Detects when a column is dropped or renamed in your governed BigQuery datasets and, because that breaks downstream queries hard, pages the on-call via PagerDuty and posts…
PR-time Snowflake schema contract check on dbt model changes
When a pull request changes a dbt model, it compares the model's declared output columns against the live Snowflake table it will replace and blocks the merge with a GitHub check…
Agent-triaged warehouse drift with impact analysis and runbook update
On a webhook from your warehouse audit log, an agent investigates the changed column, traces which downstream models and dashboards depend on it.
Cross-warehouse replication schema mismatch reconciler
Compares the column shape of mirrored tables between BigQuery and Snowflake and, when a replicated table has drifted out of sync between the two, opens an Asana task for the data…
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.