Public job postings are a real-time map of where the economy is hiring - by role, skill, location, and company. Reading that map at scale means job posting data scraping across company career pages and job boards.
This report uses web scraping of public postings to measure hiring demand by role, skill, and region, and how fast postings turn over. It is context for any recruiting-tech, research, or market team relying on job data extraction. It uses only public posting content, never applicant data.
Key findings at a glance
Three patterns stand out across the postings data. (Figures are illustrative previews - the full report breaks them down by role, skill and region.)
Key finding 1: postings map demand by role and skill
Posting volume by role, and the skills named in the text, form a live picture of labor demand - sharper and faster than lagging survey data.
This is why job posting data scraping must capture the full posting - title, description, skills, location, company - so demand can be sliced by role and skill, not just counted.
Key finding 2: hiring is geographically concentrated
Demand clusters in specific metros, and the mix differs by region. The sample shows regional concentration (illustrative).
| Region | Posting share | Leading roles |
|---|---|---|
| West | High | Software, data |
| Northeast | High | Finance, healthcare |
| South | Growing | Logistics, trades |
| Midwest | Steady | Manufacturing, ops |
Region-level job posting data scraping reveals where specific skills are in demand - essential for hiring, expansion, and research.
Key finding 3: posting turnover signals tightness
How long a posting stays open, and how often a company re-posts, signals how hard a role is to fill. Job posting data scraping that tracks postings over time - first-seen, days-open, re-post frequency - turns a snapshot into a labor-tightness signal. Only public posting content is used; applicant data is never involved.
What the underlying data looks like
The report is built from public posting records like the one below - the structure buyers receive in a sample.
{
"source": "company_career_page",
"company": "Example Corp",
"title": "Senior Data Engineer",
"role_family": "software_data",
"skills": ["Python", "Spark", "AWS"],
"location": "Austin, TX", "region": "South",
"posted_date": "2026-06-05", "days_open": 24,
"captured_at": "2026-06-29T09:00:00Z"
}
Aggregated to a role-and-region view, the data rolls up into a flat file analysts can model on:
role_family,region,postings,median_days_open,top_skill
software_data,West,4200,30,Python
healthcare,Northeast,3100,38,RN
skilled_trades,South,1800,44,Welding
Who this report is for
This report is built for the teams that read labor demand via job posting data scraping.
- Demand by role family & skill
- Regional hiring concentration
- Posting turnover & fill difficulty
- Skills-extraction methodology
- Complete methodology, sample size and sources
Methodology & data
The findings are based on job posting data scraping of public postings across company career pages and job boards in 2026, capturing title, description, skills, location and company, aggregated by role, skill and region, and tracked over time. It uses only public posting content - never applicant data. The full report details the sources, method and how each metric is calculated.
The numbers and charts shown on this page are illustrative previews of the kind of analysis in the report. They are based on publicly available, non-personal web data in aggregate and do not represent any single named company. The full report contains the complete dataset, methodology and sources.
Frequently asked questions
Yes. Enter your details and we will email you the PDF.
No. It uses only public job-posting content - never applicant or personal data.
They are illustrative previews of the report's analysis. The full PDF contains the complete dataset, methodology and sources.
Yes - tracking postings over time surfaces days-open and re-post frequency as fill-difficulty signals.
Yes. We deliver structured public posting data by role, skill and region via API or file.