Platform · Monster

Monster Data Scraping

Monster still posts jobs in the US. In Europe it stopped in the summer of 2025, and old series need to say so.

Monster data scraping collects public job postings from monster.com. Monster and CareerBuilder filed for bankruptcy in June 2025; their job boards were bought by BOLD, which runs them as separate brands, and Monster's European sites were closed. So coverage is the US site, and any history carries the ownership change and the European closures as dated events. Postings are deduplicated to one vacancy, salary stays empty where it is not published, and no candidate data, recruiter contact or login-only network is ever collected.

A series that runs through a bankruptcy and a market exit needs its breaks marked, not smoothed.

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monster_sample.jsonl LIVE FEED

{"posting_key":"pk-30117","market":"US","cross_brand_key":"cb-5521"} {"market":"DE","exit_date":"2025-07","series_break":true} {"ownership_event":"sold to BOLD","event_date":"2025-08-01"}

illustrative sample rows · USbreaks in the series · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Monster or its owners. Monster and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Monster at a glance

How we handle Monster specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Site: monster.com — BOLD since Aug 2025
Bankruptcy: Chapter 11 with CareerBuilder, Jun 2025
Europe: Sites closed Jul–Aug 2025
Coverage: US site
So: ownership and market events dated
Sister brand: CareerBuilder, same owner
Monster India: Now foundit, separate company
Refresh: Daily

Platform specifics

Breaks in the series

These are the reasons a Monster dataset needs its own handling rather than a shared retail schema.

Events in the history

Volume changes around a sale or a market exit are not labour-market signals. They are marked so they are not read as one.

market on every posting; European markets carry an exit_date.
ownership_event recorded with dates.
series_break flags on aggregates spanning the events.
cross_brand_key linking copies on CareerBuilder.

Sister sites, and what we do not do

Sister sites

Monster and CareerBuilder share an owner; see our CareerBuilder page. Monster India became foundit, a separate business.

Duplicates

Copies are collapsed to one canonical posting with every source counted, as the recruitment hub describes.

What we do not do

Smooth volume across the breaks, collect resumes or candidate data, use employer accounts, or collect recruiter contacts.

Scope

What we collect on Monster, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

US postings from public pages
Market and exit dates
Ownership events dated
Series breaks flagged
Cross-brand key with CareerBuilder
Pay only where published
Agency postings flagged
Lifecycle dates
Daily refresh

❌ What we do not, and why

Resumes or candidate data
Employer accounts
Recruiter contacts
Volume smoothed across breaks
Closed European sites presented as live

Core Monster fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
posting_key	Canonical identity
market	US; historical markets flagged
title_raw / title_normalised	As posted; taxonomy
company / company_domain	As posted; resolved
salary_min / max / currency / period	Null if unpublished
location / work_mode	As posted
is_agency_posting	Flag
cross_brand_key	CareerBuilder
ownership_event / event_date	Dated
series_break	Flag
first_seen / closed_at	Dates

Use cases

What teams do with Monster data

US hiring demand

With Monster counted once.

Board market structure

After the 2025 consolidation.

Historical research

Series with breaks marked.

Employer tracking

By domain.

The 24-hour sample — run on your sources, not ours

Send us a Monster item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Monster is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Monster data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what jobs & recruitment data covers, and a Monster-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Monster data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

The US site is, under BOLD since August 2025. Its European sites closed in summer 2025.

No. It became foundit, a separate business.

Because volume changes around the sale and closures are not labour-market signals.

Never.

Yes.

Usually part of a US board set.

Talk to us.

See real Monster data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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