This Universia scraper tutorial shows how to turn a Universia job search into a structured CSV with the Universia Job Details Scraper template for UScraper. You will import the workflow, choose the export folder, validate the first run, and fix common issues before scaling.
Before you start
Prerequisites and responsible use
You need UScraper installed as a local desktop app, the current Universia Job Details Scraper workflow, and a folder where the CSV can be written. Start with the bundled Ingeniero in Buenos Aires search before changing country, keyword, or location. A baseline run tells you whether Universia returns normal job cards and whether the export path is correct.
Universia is a jobs and internships portal, so treat the data as employment information, not as anonymous product listings. Review the live Universia portal, the current robots.txt, and the relevant privacy policy before recurring collection. This article does not cover login-only pages, private candidate records, application flows, bypassing verification screens, or republication rights.
Browser visibility is not a permission model. Keep runs deliberate, document the research purpose, and stop when the site returns access, consent, or verification states you did not plan for.
Workflow
How the Universia job scraper works
The JSON export is the authoritative workflow definition. The published template follows a readable sequence: Navigate -> Wait for Page Load -> cookie check -> Sleep -> Wait for job links -> Inject JavaScript -> Wait for hidden rows -> Structured Export. The default URL is a Universia Argentina search with keyword and location parameters, and the same pattern can be adapted to other supported Universia country paths after validation.
The extraction helper does more than scrape listing cards. It collects job detail URLs from the rendered results page, opens each matching detail page in a hidden iframe, reads JobPosting JSON-LD when available, falls back to visible labels and page text, filters cookie/footer copy, and then writes hidden .uscraper-job-detail-row elements for Structured Export.
Runbook
How to scrape Universia job details to CSV
Import the workflow
Open the Universia Job Details Scraper template, download the JSON workflow, and import it into UScraper.
Run the sample search
Keep the default Universia Argentina query for the first run. This proves the source page, waits, parser, and CSV export.
Edit keyword and location
In the Navigate block, adjust the Universia search URL. Change one dimension at a time: country path, keyword, location, then date filter.
Set the export folder
In Structured Export, confirm universia-detalles-de-empleo-scraper.csv, include headers, create mode, and a project-specific save location.
Validate the CSV
Compare several rows against the browser. Check title, employer, locality, contract, salary, description, requirements, and URL before repeating the run.
If you are looking for an Octoparse Universia alternative, keep the comparison practical. Octoparse publishes a similar Universia job details template, while UScraper is useful when an analyst wants editable local block logic and a CSV path they control. If you need hosted scheduling or managed infrastructure, a cloud scraper may still fit better.
Output
CSV export shape from the JSON workflow
There is no bundled CSV sample, so use the export shape summary and your first dry run together. The workflow writes Spanish column names because they map closely to Universia labels.
universia-detalles-de-empleo-scraper.csvColumn
titulo
Job title.
Column
empresa
Hiring organization.
Column
localidad
City, region, or country.
Column
publicidad
Published date or age.
Column
modalidad
Remote, hybrid, or in-person value.
Column
contrato
Contract type.
Column
remuneracion
Salary or visible compensation text.
Column
jornada
Workday or employment type.
Column
horario
Schedule text.
Column
descripcion
Main job description.
Column
informacion
Extra detail text.
Column
requisito
Requirements or skills.
Column
url
Source job URL.
The relevant JSON settings are short enough to inspect before you run:
{
"rowSelector": ".uscraper-job-detail-row",
"fileName": "universia-detalles-de-empleo-scraper.csv",
"includeHeaders": true,
"fileMode": "create",
"columns": [
{ "name": "titulo", "attribute": "data-titulo" },
{ "name": "empresa", "attribute": "data-empresa" },
{ "name": "localidad", "attribute": "data-localidad" },
{ "name": "descripcion", "attribute": "data-descripcion" },
{ "name": "requisito", "attribute": "data-requisito" },
{ "name": "url", "attribute": "data-url" }
]
}
Quality checks
Validation, dynamic pages, and common issues
Dynamic job board scraping fails most often because the page was not finished rendering, consent changed, or the detail page exposes less data than expected. The template waits for job links and hidden export rows, but your validation pass should still be manual.
| Symptom | Likely cause | Fix |
|---|---|---|
| Empty CSV | No export rows were created | Confirm the results page has job links and rerun with a longer wait. |
| Many blank salaries | Universia posting did not show compensation | Keep blanks rather than inventing values; review salary fields separately. |
| Wrong modality | Ambiguous remote/hybrid wording | Use explicit labels, URL hints, or description text only. |
| Duplicate rows | Same search rerun or append mode added later | Use create mode for tests, then dedupe by url for recurring research. |
| Cookie text in output | Source layout changed or filter missed footer copy | Update the filter list and rerun a small batch. |
Code-based alternatives such as Scrapy, Playwright, or open-source job scraping libraries are useful when engineers own the pipeline. UScraper fits the analyst path: visible browser execution, editable blocks, and a CSV reviewed in a spreadsheet.
Universia job pages may be publicly visible, but automated collection can still be limited by terms, robots guidance, copyright, privacy law, and employment-data rules. Review current policies, keep volume modest, avoid login-only data, and do not bypass access controls.
Use the Universia Job Details Scraper as the maintained download path, then return to the UScraper templates library for adjacent job board workflows after your first CSV is validated.