Fragmented public and non-ATS pages
BLUE-COLLAR JOB POSTING API
Job data API,built for a scoped evaluation.
Query source-linked blue-collar vacancies surfaced by a purpose-built web index, including hard-to-find jobs outside the standard ATS footprint. Evaluate the data without maintaining the indexing and normalization infrastructure yourself.
22,563 open-status records Source context retained Snapshot dated 08 Aug 2026
ILLUSTRATIVE API FORMATREAD JOB DATA
GET
/v1/active-jobs?time_frame=7d&title=electrician&location=rotterdam[
{
"id": 18421,
"title": "Maintenance Electrician",
"organization": "Example Employer",
"date_created": "2026-08-08T09:30:00Z",
"url": "https://jobs.example.com/maintenance-electrician",
"source": "bluecollarjobs",
"source_type": "career_site",
"locations_derived": ["Rotterdam, South Holland, Netherlands"],
"ai_employment_type": ["FULL_TIME"],
"ai_job_language": "nl"
}
]STRUCTURED JSONSOURCE CONTEXT RETAINED
Comparable records and fields
Classification and enrichment
Source and observation context
01 / THE BUYER PROBLEM
READ LISTINGS / DO NOT PUBLISH THEM
Build with job listings, not job-board integrations.
Many searches for a job posting API lead to tools that publish vacancies—or feeds built around the most scalable boards and ATSs. Blue Collar Job Data is for the other direction: finding fragmented blue-collar employer pages and reading those public postings as structured data.
Tell us what you need02 / WHAT YOU GET
QUERYABLE JOB DATA
A consistent record layer for products and decisions.
01
Query the records you need
Scope requests by market, occupation, employer, place and observation date instead of moving an entire dataset.
02
Sync active, modified and expired jobs
Use feed timestamps and stable public IDs to add new records, process changes and remove expired vacancies without rebuilding the dataset.
03
Keep the source attached
Preserved source context lets your team inspect the public listing behind a normalized record.
04
Build with structured JSON
Put consistent job-posting fields into recruitment products, alerts, analytics and internal workflows.
03 / AI-ASSISTED ENHANCEMENT
STRUCTURE THE EVIDENCE
AI helps make the records comparable.
AI assists with broad category classification and selected field enrichment. It does not invent vacancies. The source title, source route and observation context remain available so buyers can understand what changed between source and record.
Review the methodology04 / DATASET ANATOMY
FIELDS THAT REMEMBER THEIR SOURCE
Know what each job record contains.
These representative fields show how original values, classifications and observation history can travel together. Confirm the full field set for your requested delivery.
FIELDTYPEBUYER USE
idintegerStable public record identifier
titlestringOriginal job title retained from the source
organizationstringEmployer named on the source listing
urlURICanonical route back to the public job page
date_createddate-timeFirst successful source observation
locations_derivedstring[]Comparable job-location labels
employment_typestring[]Employment values supplied by the source
ai_employment_typeenum[]Normalized work type when supported
Need the complete field dictionary?Request a sample package matched to your market and use case.
05 / DELIVERY CHOICE
ONE RECORD LAYER / TWO WORKFLOWS
Choose access around the work you need to do.
Query narrower slices
Best for product features, alerts and workflows that retrieve records by filter or market.
Request API accessMove larger extracts
Best for warehouse imports, models, research and reproducible market snapshots.
Explore bulk delivery06 / BUYER OUTCOMES
FROM RECORD TO DECISION
Use the same data layer for different questions.
Recruitment products
Power search, matching, alerts and market-facing product features.
Workforce analytics
Compare skilled and frontline demand across places, employers and occupations.
Hiring signals
Track where employers appear to add, change or remove public vacancies.
Data and AI workflows
Supply normalized, source-linked records to models, agents and automations.
07 / FAQ
QUESTIONS DATA BUYERS ASK
Before you evaluate the job posting API.
Need a field, market or delivery detail that is not covered here? Email the team and include the workflow you are building.
[email protected]01Does this API read job listings or publish them?
It is designed to supply job-posting data for reading, analysis and software products. It does not send vacancies to job boards or applicant-tracking systems.
02Is the job posting API self-service today?
Public self-service access and documentation are still being prepared. Buyers can request sample records now and discuss a scoped API evaluation with the team.
03How does AI enhance the data?
AI assists with tasks such as broad category classification and selected field enrichment. It does not generate job listings, and source evidence remains attached to the record. Taxonomy and field-level limits are confirmed per delivery.
04What market is represented in the current snapshot?
The dated snapshot contains 21,533 Netherlands records and a separately scoped 1,030-record United States pilot. Any sample or commercial discussion should state the market, snapshot date, source definition and known exclusions.
05Can I receive the same job data in bulk?
Yes. Bulk delivery is the better fit when you need a larger extract for a warehouse, model, research project or recurring analysis.
08 / NEXT STEP
START WITH REAL RECORDS
See whether the data fits your product.
Tell us the market, occupations and workflow you want to evaluate. We will use that context to shape the most relevant sample and answer the important coverage questions before a sales call.
SAMPLE REQUEST / INCLUDE
- MARKET
- Country or region
- OCCUPATIONS
- Trades or frontline roles
- WORKFLOW
- Product, analysis or research
- DELIVERY
- API evaluation