Recruiting teams
Shortlist review
Favorable to scraping
Export selected InfoJobs roles into one spreadsheet, filter by location, modality, salary, and contract type, then open the source URLs only for offers that match the hiring brief.
Job Boards$50Free
InfoJobs Details Scraper
The InfoJobs Details Scraper turns approved InfoJobs job detail URLs into a structured CSV for recruiting research, labor-market analysis, and spreadsheet workflows. Import the template into UScraper, paste the offer URLs you want to review, and export job title, company, rating, location, work mode, salary, contract, studies, description, category, level, vacancies, and extraction status without building a custom InfoJobs data extractor.
Output
CSV file
Columns
17
Input
Detail URLs
Fallback
Status row
Source
InfoJobs
At a glance
Extract InfoJobs job details from URLs
Build offer-level job datasets
Capture the fields analysts normally copy by hand from InfoJobs detail pages, including employer, location, work mode, salary, minimum experience, contract type, required studies, category, level, and vacancies.
Use a controlled URL list
The workflow starts from known job URLs instead of open-ended crawling, which makes it useful for curated market maps, competitor hiring checks, and approved sourcing research.
Keep verification visible
InfoJobs can show robot checks. This template records captcha_or_robot_check_detected when that happens and keeps safe fallback values such as the source URL.
Prefer structured page data
Export columns read JobPosting JSON-LD where available, then fall back to visible page text so the CSV remains useful across common InfoJobs page variants.
Who uses it
Use cases for InfoJobs job scraper exports
Labor-market analysts
Weekly snapshots
Favorable to scraping
Save repeatable job detail rows for Spanish market research. The empleo_url and tiempo_de_publicacion columns help keep each observation tied to a source and date signal.
Data operations teams
Enrichment queue
Favorable to scraping
Use this InfoJobs data extractor as a first pass, then enrich employer URLs with domains, sector tags, recruiter notes, translations, or internal scoring.
Pair this detail workflow with the InfoJobs Listing Scraper when you need a source list first. For broader employment research, compare rows with the France Travail Details Scraper, LinkedIn Job Details Page Scraper, and the full UScraper template library.
How to use
Scrape InfoJobs job details in five steps
2
Add approved job URLs
Replace the two sample InfoJobs URLs in the Navigate block with the job detail pages your team is allowed to review.
3
Confirm waits and export path
The workflow waits up to 45 seconds for a page load, branches around robot-check signals, and writes to infojobs_detalles_scraper.csv in append mode.
4
Run the URL loop
UScraper opens each URL, waits or pauses based on the page state, exports the configured columns, then uses Loop Continue to advance to the next detail page.
5
Open and verify the CSV
Spot-check several rows against InfoJobs before using the file for sourcing, market analysis, reporting, or downstream enrichment.
Output preview
What the InfoJobs CSV includes
The export mirrors the workflow definition and writes one row per supplied detail URL. When InfoJobs returns a normal job page, the template captures job fields from structured data and visible text. When verification blocks extraction, the row still records the status and source URL so the batch is auditable.
| extraction_status | empleo | empresa | ubicacion | modalidad | salario | tipo_de_contrato | numero_de_vacantes |
|---|---|---|---|---|---|---|---|
| ok | Mozo conductor lavador vehiculos | Empresa de movilidad | Malaga, Andalucia, ES | Presencial | 18.000 EUR - 21.000 EUR | Contrato indefinido | 2 |
| ok | Lavador conductor vehiculos Madrid | Servicio de automocion | Madrid, Comunidad de Madrid, ES | Presencial | Salario no disponible | Jornada completa | 1 |
| captcha_or_robot_check_detected | LAVADOR CONDUCTOR VEHICULOS MADRID | Madrid |
infojobs_detalles_scraper.csvColumn
extraction_status
ok, captcha_or_robot_check_detected, or loaded_but_job_selectors_not_confirmed.
Column
empleo
Job title from JobPosting data, page heading, or URL-derived fallback.
Column
empleo_url
Full InfoJobs offer URL for source verification.
Column
empresa
Employer name when available.
Column
empresa_url
Company profile URL or sameAs value when available.
Column
calificacion
Rating or qualification text parsed from the page when present.
Column
ubicacion
City, region, and country from structured job location or visible page text.
Column
modalidad
Work mode such as Presencial, Hibrido, Teletrabajo, or Remoto.
Column
tiempo_de_publicacion
Posting date or publication freshness text.
Column
salario
Salary amount, range, currency, or salary text when exposed.
Column
experiencia_minima
Minimum experience requirement.
Column
tipo_de_contrato
Contract or employment type.
Column
estudios_minimos
Minimum studies or education requirement.
Column
descripcion
Cleaned job description.
Column
categoria
Occupational category or industry.
Column
nivel
Seniority or level text when available.
Column
numero_de_vacantes
Vacancy count parsed from the page.
Headers included - one row per InfoJobs detail URL
Comparison
Local InfoJobs scraper vs hosted job data tools
This UScraper template
LocalHosted job data APIs and cloud actors
CloudRuntime
Runs in the desktop app
You can watch pages load, adjust waits, and review blocked URLs during a controlled batch.
Runs on vendor infrastructure
Useful for managed ingestion, but logs, quotas, and execution details live outside your workstation.
Output custody
Writes CSV to your folder
Good for spreadsheet-first teams that need local review before sharing or enrichment.
Exports through dashboards or APIs
Often requires accounts, recurring credits, and separate data-retention review.
Best fit
Curated InfoJobs detail research
Best for supplied URLs, light monitoring, and auditable market snapshots.
Large managed collection
Better when you need a vendor to operate high-volume infrastructure.
Frequently asked questions
InfoJobs job pages may expose public offer data, but automated collection can still be restricted by InfoJobs terms, robots directives, copyright, database rights, privacy law, and employment-data rules. Review the platform policies, keep runs modest, avoid bypassing access controls, and get legal advice before using exports commercially.
Before you scale
Limits and maintenance notes
Practical guardrails for InfoJobs detail exports
Rate limits
Run measured batches instead of aggressive loops
Keep the bundled waits, avoid parallel runs, and increase delay if pages load slowly or InfoJobs shows verification prompts. A job detail dataset is easier to trust when each batch is deliberate and reviewable.
Layout drift
Offer-page markup can change
The template uses structured data first, then visible page text. If rows become blank, filled with fallback titles, or missing salary and contract values, update the Structured Export columns before collecting more data.
Policy
Respect InfoJobs terms and public-page boundaries
Local custody helps with data handling, but it does not create permission to over-collect, republish, or resell job listings. Review InfoJobs policies, robots guidance, and your own compliance requirements before commercial use.
Get Started
Download and use this template instantly
$50Free
What's Included
- Template JSON file ready to import
- Pre-configured scraping nodes
- Works with UScraper desktop app
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