DATA OPS

Open Linear Issues for Untagged PII Columns in BigQuery

Scans BigQuery columns flagged as sensitive but missing a Data Catalog policy tag, then files a Linear issue per owning team so the tagging gap gets fixed and tracked.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled audit run
  • ActionQuery BigQuery columns vs policy tagsGoogle BigQueryBigQuery
  • LogicGroup untagged PII columns by owning team
  • LogicDedupe against existing open Linear issues
  • ActionCreate Linear issue per teamLinearLinear
  • OutputEmit run-complete summary

What it does

It finds columns in BigQuery that look like PII but have no policy tag applied, then turns each governance gap into a tracked Linear issue assigned to the dataset's owning team. Instead of a Slack message that scrolls away, every untagged sensitive column becomes an accountable, closeable ticket.

When to use it

Use it when your org enforces column-level access control through BigQuery policy tags and you need to guarantee that every sensitive column is actually tagged. It converts a passive audit finding into owned remediation work.

How it works

  1. 1A scheduled trigger starts the audit run.
  2. 2Query BigQuery `INFORMATION_SCHEMA.COLUMNS` joined against policy-tag metadata to list columns whose names match PII patterns but carry no tag.
  3. 3A logic step groups the untagged columns by dataset and resolves the owning team from a label or naming convention.
  4. 4For each group, deduplicate against existing open issues so reruns don't spam.
  5. 5Create one Linear issue per owning team listing the affected tables and columns with a remediation checklist.
  6. 6Emit a run-complete summary noting how many issues were opened or already existed.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect LinearIssues, projects, cycles, triage.
  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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