JOB POSTINGS DATA

Hiring is a signal.
Read it at scale.

// THE SHORT ANSWER

Job postings data captures public hiring activity at scale — job titles, descriptions, locations, salary ranges, required skills and posting velocity — from company career pages and public job boards. iWeb Data Scraping structures this into panels investors use to read hiring velocity as a growth signal, researchers use to map labor markets, and HR-tech products build on. Because a company's hiring is visible weeks before it appears in any filing, job data is one of the most established alternative-data signals — and it's public, PII-free by construction.

Get free sample in 48h See delivery specs
99%+field accuracy, QA-verified
48hfree sample turnaround
24/7pipeline monitoring
ISO 27001+ 9001 certified

Key facts

  • Sources: Public company career pages + public job boards; custom source lists
  • Fields: Title, role category, location, salary (if posted), skills, first/last-seen, company/ticker
  • Integrity: Point-in-time timestamps for backtest-safe trend analysis
  • Delivery: CSV/Parquet, API, warehouse-direct

What a company hires for, where, and how fast is one of the clearest windows into its strategy — and it's fully public. A surge in engineering roles signals product investment; sudden hiring freezes precede trouble; geographic patterns reveal expansion before any announcement. Hiring velocity is a classic alternative-data signal precisely because it leads the disclosed numbers.

We collect job postings from public company career pages and job boards, structuring each into clean fields — title, normalized role category, location, salary range where posted, required skills, and first-seen/last-seen dates for velocity. Entities map to companies (and tickers, for investors). The data is PII-free by construction — it's about roles and companies, not individuals — which keeps it clean of the privacy risk that dogs people-data scraping.

WHAT'S INCLUDED

Hiring, as a structured
time series.

Full posting details

Title, description, location, employment type and salary range where disclosed — structured, not free-text dumps.

Role normalization

Messy titles mapped to standard role categories, so 'Sr. SWE II' and 'Senior Software Engineer' count as one thing.

Skills extraction

Required and preferred skills parsed from descriptions — track demand for specific technologies or competencies.

Posting velocity

First-seen and last-seen dates give you hiring pace, role duration and freeze detection over time.

Company & ticker mapping

Postings resolved to companies and, for investors, to listed tickers — hiring signal by name.

Point-in-time history

Timestamped as-observed, so hiring trends are backtest-safe for alt-data models.

SEE THE DATA FIRST

What you'll actually receive.

Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.

Hiring panel — normalized roles, skills, velocity, ticker-mapped.
first_seen company ticker role_category location skills status
2026-07-02 ExampleTech EXMPL Software Engineer Bengaluru Python, AWS open
2026-07-02 ExampleTech EXMPL Data Scientist Remote ML, SQL open
2026-06-15 ExampleTech EXMPL Sales Manager Mumbai B2B SaaS filled
2026-07-05 RivalCo RIVL DevOps Engineer Pune K8s, Terraform open
↑ Sample structure — illustrative values. Your data reflects your platforms and category. Get this for your data →
DELIVERY SPECS

The details procurement asks for.

SOURCES Public company career pages + public job boards; custom source lists
FIELDS Title, role category, location, salary (if posted), skills, first/last-seen, company/ticker
INTEGRITY Point-in-time timestamps for backtest-safe trend analysis
DELIVERY CSV/Parquet, API, warehouse-direct
TIMELINE Free sample in 48h · production 1–2 weeks · panels 2–4 weeks by universe
PRICING By companies/universe × sources × frequency
COMPLIANCE Public postings only · PII-free by construction · ISO 9001/27001 · NDA-first

Every engagement is NDA-first and starts with a free sample — judge the data before any commitment.

THE HONEST COMPARISON

Why a data layer, not a DIY script or a rigid tool

In-house DIY Generic SaaS tool iWeb Data Scraping
Setup & maintenance You build scrapers, fight anti-bot, fix breakages weekly Rigid templates, breaks on site changes, slow support Fully managed — we build, monitor and fix, you never touch a proxy
Data quality Best-effort, no QA layer, silent failures Generic parsers, frequent gaps 99%+ field accuracy, QA-verified, monitored 24/7
Coverage Limited to what you can maintain Only supported sites Any public site or app, at scale
Compliance Your legal risk to manage alone Often opaque about methods ISO-certified, PII-scrubbed, NDA-first, documented
Time to value Weeks to months of engineering Fast but inflexible Free sample in 48h, production in days
WHO USES THIS

Built for the person
who owns the number.

INVESTOR / ALT-DATA

Hiring velocity as growth signal

Engineering surges, expansion patterns and freezes on covered tickers — a classic leading indicator, delivered point-in-time. See alternative data.

LABOR / MARKET RESEARCH

Map demand for skills & roles

Which skills are heating up, where roles concentrate, how compensation shifts — labor market as a dataset.

HR-TECH PRODUCT

Power your platform

Structured job feeds behind recruiting, comp-benchmarking or market-mapping products — you build, we supply the data.

RELATED:Alternative Data →Company Datasets →News Data Feeds →
FAQ

Before the first call.

Because hiring leads disclosure. A company's job postings reveal investment priorities, expansion plans and stress weeks or months before they appear in filings or announcements. Engineering-role surges, geographic expansion and hiring freezes are all readable in public postings, which is why hiring velocity is one of the oldest, most trusted alternative-data signals.

Yes, cleanly — job postings are public content published by companies to attract applicants, and the data is about roles and companies, not individuals, so it's PII-free by construction. This sidesteps the privacy risks that affect people-data scraping. We collect from public career pages and boards under documented, NDA-first methodology.

Yes — role normalization maps the messy reality of job titles ('Sr. SWE II', 'Senior Software Engineer', 'Software Engineer III') to standard categories, so your analysis counts roles consistently across companies. We also extract required skills from descriptions for technology- and competency-level demand tracking.

Yes — postings are timestamped point-in-time with first-seen and last-seen dates, and history is not restated. That gives you accurate hiring velocity and role-duration trends as they would have been observed on each date, which is essential for building alt-data models without lookahead bias.

Get a free sample dataset in 48 hours.