InfoJobs job pages are useful for more than recruiting. A structured InfoJobs hiring data scraper can support market research, newsroom reporting, SEO content planning, and hiring-monitoring workflows when the output is a reviewable CSV. The practical starting point is the InfoJobs Details Scraper template for UScraper, which turns a controlled list of job detail URLs into columns analysts can filter.
Problem
Why InfoJobs job market data is hard to use manually
InfoJobs is a high-signal source for Spain hiring research because job detail pages combine employer, role, location, contract, salary, and requirement signals in one place. The 2025 InfoJobs-Esade labor market report described nearly 2.5 million vacancies and 4.2 million people registered for at least one vacancy, while El Pais later highlighted the pressure of 56 candidates per offer in InfoJobs data. For analysts, that kind of market volume is exactly why copy-paste breaks down.
The pain is not opening one page. The pain is keeping rows consistent after the tenth, fiftieth, or hundredth page. Manual notes lose source URLs. Salary values get mixed with description text. A newsroom researcher may need to compare disclosed pay by region; an SEO team may need recurring examples of job-title language; a hiring team may need competitor openings grouped by city and contract type.
A useful InfoJobs export is not just scraped text. It is a row shape your team can audit, sort, and defend later.
Personas
Use cases for an InfoJobs hiring data scraper
Labor-market research
Track selected occupations across cities, then compare ubicacion, modalidad, salario, tipo_de_contrato, and experiencia_minima in a spreadsheet.
Newsroom evidence collection
Build a sourced table behind a story about hiring demand, salary disclosure, seasonal roles, remote work language, or vacancy pressure in a specific sector.
SEO and content planning
Collect real job-title phrases, category names, location modifiers, and skills language so employment content matches how roles are actually published.
Competitor hiring monitoring
Review known employer offer URLs weekly and flag new roles, contract changes, salary bands, and work-mode shifts without rebuilding a custom parser.
Outcome
What the InfoJobs Details Scraper exports
The template is built around job detail URLs, not open-ended search crawling. The JSON workflow shows a clear path: Set Window Size -> Navigate -> Wait for Page Load -> Element Exists -> Sleep -> Structured Export -> Loop Continue. The export runs in append mode, so every supplied URL can write one row to the same CSV.
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, the page heading, or safe URL fallback.
Column
empleo_url
The exact InfoJobs detail URL opened for the row.
Column
empresa
Hiring organization name when exposed.
Column
ubicacion
City, region, or location text from structured or visible page data.
Column
modalidad
Presencial, hybrid, remote, or equivalent work-mode text when available.
Column
salario
Salary text or structured salary range when present on the job page.
Column
descripcion
Cleaned job description text for qualitative review.
The full workflow also exports empresa_url, calificacion, tiempo_de_publicacion, experiencia_minima, tipo_de_contrato, estudios_minimos, categoria, nivel, and numero_de_vacantes. Those fields matter because they separate the role, employer, requirements, and market context instead of leaving everything inside one copied description.
| Research question | CSV columns to start with | Example output decision |
|---|---|---|
| Are employers disclosing salary? | salario, categoria, ubicacion | Count salary-present rows by category and city. |
| Is hybrid work increasing in a niche? | modalidad, tiempo_de_publicacion, empresa | Compare work-mode language across repeat snapshots. |
| Which roles need seniority? | experiencia_minima, nivel, descripcion | Filter offers that mention years, seniority, or management. |
| Are competitors expanding? | empresa, empresa_url, empleo, numero_de_vacantes | Track employer-specific openings over time. |
Workflow
From URL list to monitoring CSV
The best workflow is deliberately small at first. Create a source list of approved InfoJobs detail URLs, import the InfoJobs Details Scraper, run one URL, and compare the CSV against the browser. If the row looks right, run a small batch and filter by extraction_status before doing any analysis.
For recurring monitoring, keep three files together: the original URL list, the exported CSV, and a notes file describing the research question, run date, and any filters. That lightweight audit trail helps a newsroom editor, client, or internal stakeholder understand where the numbers came from.
API fit
InfoJobs API alternative or official integration?
InfoJobs has developer documentation for application authentication, OAuth 2.0 user authorization, offer lists, and offer details. The offer detail operation is documented as GET /offer/{offerId}, returning the detail of the offer identified by the URL parameter. If you are building an approved app, syncing product data, or need a documented contract, start with the official InfoJobs developer documentation.
UScraper fits a different lane: browser-visible, analyst-led CSV collection from known job detail URLs. That makes it a practical InfoJobs API alternative for one-off research, monitored URL lists, and spreadsheet-first workflows where the team needs to inspect the rendered page and local export.
| Need | Better route | Reason |
|---|---|---|
| Approved product integration | InfoJobs API | Credentials, documented endpoints, and app authentication. |
| Small research CSV | UScraper template | Local workflow, visible browser state, and append-mode export. |
| Hosted scheduling | Cloud scraper or data provider | Remote runs, APIs, queues, and vendor-managed infrastructure. |
| Published market claims | Policy and legal review first | Collection method does not decide reuse rights. |
FAQ
InfoJobs hiring data FAQ
Recruiting researchers, labor-market analysts, newsrooms, SEO teams, and monitoring teams use exports when they need comparable rows for job titles, companies, locations, salaries, contract types, descriptions, and source URLs.
For more template-led workflows, browse the UScraper template library or the latest articles in the UScraper blog.