# Scrape Job Postings — Custom Dataset, $1 Free

> Scrape job postings from career pages and job boards into one dataset: roles, companies, locations, dates. No per-site scrapers to maintain. $1 free monthly.

[Home](https://quanticdata.io/)/[AI Web Scraping Service](https://quanticdata.io/ai-web-scraping-service/)/*Scrape Job Postings*

# Scrape job postings into a dataset you define

Job postings data powers labor analytics, sales triggers and market maps — but vendors sell it as billion-row licenses. Quantic AI scrapes the slice you define instead: roles, companies, locations and posted dates from career pages and public boards, discovered through the Google Jobs vertical first.

[Get my free API key](https://quanticdata.io/signup/) [Meet Quantic AI](https://quanticdata.io/ai-web-scraping-service/)

$1 free every month · No card required · Blocked pages are never billed

hiring pipeline · example run

```
# "data engineer postings in Berlin, last 30 days"
search("data engineer berlin", jobs vertical)
→ postings + hiring companies · $0.0005
map(company sites) · find /careers, /jobs
→ career sections located · $0.0005 each
scrape(postings, extract: title, salary, date)
→ rows: { "title": "Data Engineer",
    "company": "…", "posted": "2026-07-14" }
```

rolecompanyposted

data engineerfintech · berlinjul 14

data engineer, srlogistics · berlinjul 11

analytics engineersaas · remote-dejul 09

**Jobs vertical**Google Jobs results as structured JSON

**Career pages**the primary source, mapped automatically

**Your slice**role × geo × date window, your columns

**No license**cents per run, pay per success

How it works

## From a hiring question to a postings table

### 1 · Ask the market

"Data engineer postings in Berlin", "who's hiring sales in Milan fintech", "remote React roles posted this month". The [SERP API](https://quanticdata.io/serp-api/)'s Google Jobs vertical returns postings and the companies behind them as structured JSON.

### 2 · Go to the source

For each hiring company, a map call finds /careers and /jobs sections — the postings' primary home, where detail is richest and freshest. No board markup in the way.

### 3 · Extract the posting

Each posting page is scraped with your schema via the [scraping API](https://quanticdata.io/web-scraping-api/): title, location, posted date, salary where stated, requirements. Rows are deduplicated across boards and career pages.

### 4 · Track the signal

Rerun weekly as a batch job and diff: new roles are sales triggers, closed ones are churn signals, counts by company map your competitors' growth — your slice, on your schedule.

## A job posting data API for pipelines

Everything above is callable from code: the jobs vertical, map and scrape as REST endpoints or [MCP tools](https://quanticdata.io/web-data-api-for-ai/) — the same [AI web scraping service](https://quanticdata.io/ai-web-scraping-service/) pipeline that runs from a prompt. Labor-analytics and sales-intelligence teams can feed their models without negotiating a catalog license.

Batch jobs keep a fixed company list refreshed — up to 1,000 URLs per job, webhooks on completion, unused items refunded — and every response carries its own cost, so the data product's margin is visible per row.

Pipeline-grade *jobs vertical* *batch + webhooks* *REST* *MCP* *cost per response*

## Scrape job postings from the open web only

Postings are public by intent — companies publish them to be found. This pipeline honors the boundary anyway: **career pages, public ATS pages and open boards yes; logged-in platforms no**. No LinkedIn credentials, no walled-garden workarounds.

That boundary is also why the data is cleaner: a posting on the employer's own site has no aggregator lag, no repost duplication, and states exactly what the company wrote — with the source URL in the row to prove it.

Primary source the employer's own page — no aggregator lag, no repost noise

## Your slice vs a typical postings data vendor

|  | Quantic AI dataset | Typical postings vendor |
| --- | --- | --- |
| Scope | role × geo × window you define | their full catalog, filtered |
| Buying | self-serve, cents per run | licensing negotiation |
| Schema | your columns | their taxonomy |
| Freshness | collected at request time | their crawl cycle |
| Source | career pages, URL per row | aggregated, provenance varies |

### What does a weekly hiring scan cost?

With Quantic AI (planned): 120 postings × $0.03 ≈ **$3.60 per weekly scan** plus metered pipeline; refreshes of known career pages drop to $0.01 per record. On the raw APIs the same run meters at about $0.05 — code on you.

[Get my free API key](https://quanticdata.io/signup/)

## FAQ

The short answers on pricing, formats and setup.

[Something else? Ask us →](mailto:hello@quanticdata.io)

### Where can I get job postings data?

Three routes: government statistics (BLS, Eurostat — aggregated, months behind), enterprise vendors licensing billion-row catalogs, or collecting the slice you need from public sources yourself. This page is the third route, automated: the Google Jobs vertical finds postings, career pages fill in the detail.

### Is there a free job postings dataset?

Public statistics are free but aggregated — you can't see individual roles or companies. Here, $1 of usage renews free every month: on the raw APIs that's thousands of scraped pages, and at Quantic AI's planned record pricing it's a few dozen postings — enough to sample either path before paying.

### Can I just use an open-source job scraper from GitHub?

You can, and for one or two boards it works. The cost shows up later: open-source job scrapers break whenever a board changes its markup, they ship without proxies so the big boards block you by volume, and nobody is on call when a nightly run returns empty. This runs the same idea as a service — search finds the postings, extraction reads them, the proxy network absorbs the blocking, and failed calls are never billed.

### How do I scrape job postings with Python?

Call the API from Python instead of building the stack: one request to the search endpoint with the jobs vertical returns the postings, one [scrape](https://quanticdata.io/web-scraping-api/) call per career page returns clean Markdown or the exact fields you name. That replaces requests plus BeautifulSoup plus a proxy pool plus retry logic. The same endpoints are also [MCP tools](https://quanticdata.io/mcp-server/), so a Python agent can drive them itself.

### What's the difference between BLS statistics and raw postings data?

BLS-style statistics count openings by sector after the fact — clean, official, aggregated. Raw postings are the openings themselves: title, company, location, requirements, posted date. You need the raw rows for sales triggers, salary benchmarks, competitor hiring maps or skills analysis.

### Can it scrape LinkedIn or Indeed?

No — walled boards behind logins are out of scope by design. The pipeline reads the Google Jobs vertical and the open web: company career pages, public ATS pages and boards that don't gate content. For hiring-signal analysis, career pages are the primary source anyway.

### What fields does a postings dataset include?

Title, company, location, posted date, source URL and — where the posting states them — salary range, seniority, remote policy and required skills. You name the columns in the prompt; extraction returns exactly those fields as CSV or JSON.

### How much does job postings data cost here?

With Quantic AI, planned pricing: $0.03 per delivered posting — a weekly 120-posting scan lands around $3.60 plus metered pipeline, refreshes at $0.01 per record. No license, no annual contract. Developers can meter the same scan on the raw APIs for about $0.05 in unit prices.

## The hiring market, in your columns

Start with $1 free every month — or join the Quantic AI waitlist for the full prompt-to-dataset service.

[Get my free API key](https://quanticdata.io/signup/)

Keep exploring: [AI Web Scraping Service](https://quanticdata.io/ai-web-scraping-service/) [Company Data](https://quanticdata.io/scrape-company-data/) [Real Estate Data](https://quanticdata.io/real-estate-data-scraping/) [SERP API](https://quanticdata.io/serp-api/) [Web Scraping API](https://quanticdata.io/web-scraping-api/)

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Source: https://quanticdata.io/scrape-job-postings/ · Site index for AI: https://quanticdata.io/llms.txt