Jira + Bruin

Source

Ingest Jira data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Operational analytics
Jira data in your warehouse means analytics that Jira's built-in reporting can't provide. Cross-tool, cross-team, custom.
Cross-tool project views
Combine Jira with Jira, GitHub, Slack, and other tools. One dashboard that shows the real state of projects.
Team workload insights
Understand collaboration patterns, bottlenecks, and workload distribution from Jira data — automatically updated.
No manual data pulling
Jira data syncs on schedule. Managers and leads get fresh data without asking anyone.

For data & engineering teams

How it works

Incremental sync
Only sync new and changed Jira records. No full reloads, no wasted compute.
YAML-defined, Git-versioned
Your Jira pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Schema change handling
Bruin detects Jira schema changes automatically. No manual intervention when fields get added or renamed.
Cross-tool joins
Combine Jira data with other tools in SQL transforms. Bruin resolves dependencies across sources automatically.

Before you start

Jira Cloud account

API token from Atlassian account

Project access permissions

Step 1

Add your Jira connection

Connect using Jira Cloud credentials. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

domainJira domain (e.g., company.atlassian.net)
emailEmail address for authentication
api_tokenAPI token for Jira Cloud

connections:
  jira:
    type: jira
    uri: "jira://?domain=<domain>&email=<email>&api_token=<api_token>"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Jira and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

Available tables

projectsissuesusersissue_typesstatusesprioritiesresolutions

name: raw.jira_projects
type: ingestr

parameters:
  source_connection: jira
  source_table: 'projects'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Jira data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Validate workspace data synced completely

Ensure record IDs are unique and titles are present

Catch missing or null fields on every sync

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: title
    checks:
      - name: not_null

custom_checks:
  - name: workspace sync is complete
    query: |
      SELECT COUNT(*) > 0
      FROM raw.jira_projects

Step 4

Run it

One command. Bruin connects to Jira, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Jira to your dashboards

$ bruin run .

Running pipeline...

  jira_projects
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

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Ready to connect Jira?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs