JOB POSTINGS DATASET

Bulk job data,ready for serious analysis.

Bring a normalized job postings dataset into your warehouse, model or research workflow. Receive structured blue-collar vacancy records with source context, observation fields and an explicit coverage definition.

Request dataset sample See what is included

22,563 active records Source context retained Snapshot dated 08 Aug 2026

SAMPLE DATASETCSV / JSON

JOB IDTITLECITYLAST SEEN

job_01ElectricianRotterdam08 Aug

job_02Service technicianUtrecht08 Aug

job_03CNC operatorEindhoven07 Aug

job_04Warehouse leadTilburg07 Aug

FIELD DICTIONARY INCLUDEDCOVERAGE DEFINED

01SOURCE-DIRECT

Public employer career pages

02NORMALIZED

Comparable records and fields

03AI-ENHANCED

Classification and enrichment

04TRACEABLE

Source and observation context

01 / THE BUYER PROBLEM

THE DATA, WITHOUT THE COLLECTION LAYER

Start with structured records, not scraped pages.

Public vacancies are spread across employer websites, local career pages and different application systems. A useful bulk dataset brings those records into a shared structure and makes its scope understandable.

Tell us what you need

02 / WHAT YOU GET

ANALYSIS-READY DELIVERY

A job listings dataset with the context to use it.

Load analysis-ready files

Move normalized records into a warehouse, notebook, model or BI environment without starting from scraped pages.

Compare consistent fields

Work across employers and source layouts with shared occupation, location and observation fields.

Define a useful time window

Scope a delivery around the snapshot or observation history your analysis actually requires.

Receive the context too

Evaluate records with a field dictionary, coverage note and methodology rather than a file with unexplained columns.

03 / AI-ASSISTED ENHANCEMENT

STRUCTURE THE EVIDENCE

AI helps make the records comparable.

AI assists with occupation classification, location normalization 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 methodology

04 / 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

job_idstring

Stable normalized record identifier

titlestring

Original job title retained from the source

companystring

Employer named on the source listing

citystring

Normalized location for filtering and comparison

occupationstring

Consistent occupation classification

first_seendate-time

First successful source observation

last_seendate-time

Most recent successful source observation

source_urlURL

Route back to the public source page

Need the complete field dictionary?Request a sample package matched to your market and use case.

Get the field list

05 / DELIVERY CHOICE

ONE RECORD LAYER / TWO WORKFLOWS

Choose access around the work you need to do.

JOB POSTING API

Query narrower slices

Best for product features, alerts and workflows that retrieve records by filter or market.

Explore API access

BULK JOB DATA

Move larger extracts

Best for warehouse imports, models, research and reproducible market snapshots.

Request bulk data

06 / 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 a bulk job dataset.

Need a field, market or delivery detail that is not covered here? Email the team and include the workflow you are building.

[email protected]

01What is included in a job postings dataset?

A scoped dataset can include normalized job records, source context, observation fields, a field dictionary, a coverage note and methodology. Exact fields depend on the agreed extract.

02Which file formats are available?

CSV and JSON are available for evaluation samples. Commercial delivery format and cadence are agreed around the buyer's workflow rather than implied by a generic download button.

03Is historical job data available?

Records contain observation fields such as first seen and last seen. Historical depth and lifecycle definitions should be confirmed for the specific market and delivery before purchase.

04How is AI used in the dataset?

AI assists with classification, normalization and selected enrichment. The underlying vacancies come from public employer sources; they are not generated by AI.

05When should I choose bulk data instead of the API?

Choose bulk delivery for large analysis workloads, warehouse imports and reproducible snapshots. Choose API access when a product or workflow needs narrower, query-based retrieval.

08 / NEXT STEP

START WITH REAL RECORDS

See whether the dataset fits your analysis.

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.

Request dataset sample Review current coverage

SAMPLE REQUEST / INCLUDE

MARKET: Country or region
OCCUPATIONS: Trades or frontline roles
WORKFLOW: Product, analysis or research
DELIVERY: CSV or JSON sample

Email [email protected]