Bullhorn + Bruin
Ingest Bullhorn 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
People analytics beyond HR tools
Join Bullhorn data with finance and project data. See fully-loaded team cost, hiring ROI, and attrition trends.
Headcount planning with real data
Combine Bullhorn org data with budget and project data. Plan headcount based on actual numbers, not estimates.
Compliance-ready data
Quality checks validate that required fields are present, records are consistent, and org hierarchy is valid.
Faster reporting cycles
Bullhorn data syncs automatically. HR and finance get fresh data without waiting for someone to pull a report.
For data & engineering teams
How it works
Automatic schema handling
Bruin detects Bullhorn schema changes and handles them automatically. No manual migration scripts.
YAML-defined, Git-versioned
Your Bullhorn pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Hierarchy validation
Custom SQL checks validate manager-employee relationships and catch orphaned records in Bullhorn org data.
Incremental sync
Only sync new and changed Bullhorn records. Full org structure stays in sync without re-processing everything.
Before you start
Step 1
Add your Bullhorn connection
Connect using OAuth client credentials. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.
Parameters
client_idBullhorn API client IDclient_secretBullhorn API client secret
connections:
bullhorn:
type: bullhorn
uri: "bullhorn://?client_id=<id>&client_secret=<secret>"Step 2
Create your pipeline
Define a YAML asset that tells Bruin what to pull from Bullhorn and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.
Available tables
name: raw.bullhorn_candidates
type: ingestr
parameters:
source_connection: bullhorn
source_table: 'candidates'
destination: bigqueryStep 3
Add quality checks
Add column-level and custom SQL checks to your Bullhorn data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.
columns:
- name: employee_id
checks:
- name: not_null
- name: unique
- name: status
checks:
- name: accepted_values
value: ['active', 'inactive', 'terminated', 'on_leave']
custom_checks:
- name: valid manager hierarchy
query: |
SELECT COUNT(*) = 0
FROM raw.bullhorn_candidates
WHERE manager_id IS NOT NULL
AND manager_id NOT IN (SELECT employee_id FROM raw.bullhorn_candidates)Step 4
Run it
One command. Bruin connects to Bullhorn, 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.
--start-date$ bruin run .Running pipeline...
bullhorn_candidates
✓ 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 12sReady to connect Bullhorn?
Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.
