InfoJobs Spain Job Listings Scraper

InfoJobs Job Scraper 🇪🇸 is an evidenced Store rival with a Spain-focused promise to extract job titles, descriptions, and company details for market analysis and recruitment research. This actor addresses the same buyer intent with a more explicit workflow around configurable queries or startUrls, bounded runs, proxy settings, and inspection of reusable Apify dataset records; it does not claim broader country coverage or richer fields than the source returns.

Brazil Jobs Scraper — InfoJobs, Vagas, Gupy, LinkedIn is another evidenced rival, positioned around combining five Brazilian job boards and fields such as salary, benefits, experience level, and work model. That breadth is useful for cross-board Brazilian research, while this actor is intentionally narrower and better suited to buyers who need focused InfoJobs collection, repeatable search inputs, and a clearly documented validation step instead of an unsupported promise of multi-board coverage.

What this actor does

The actor collects job-search results from InfoJobs and writes structured records to the Apify dataset. It is suitable for:

• Recruitment research and talent-market discovery.
• Comparing recurring searches by role, company, or location.
• Employment and job-market monitoring.
• Building a bounded input dataset for spreadsheets, warehouses, or internal automation.
• Exporting collected records as JSON or CSV after review.

The available inputs are:

• queries — search terms or query definitions accepted by the current actor schema.
• startUrls — InfoJobs search URLs to collect.
• maxItems — an upper bound for a run.
• proxyConfiguration — proxy settings appropriate for the access environment.

Always open the current input schema before running. Input names, accepted shapes, and returned fields are implementation details and may change.

Recommended workflow

1. Start with one narrowly scoped query or search URL.
2. Set a small maxItems value for validation.
3. Configure a suitable proxy only when required by the source or your operating environment.
4. Run the actor and inspect several dataset records.
5. Confirm which title, description, company, location, salary, and URL fields are present for your search.
6. Adjust the query or limit, then export the verified dataset or connect it to downstream processing.
7. For monitoring, retain the query, URL, run date, and filtering choices so later runs remain comparable.

Output and dataset use

The run writes records to the default Apify dataset. The exact schema depends on the source response and actor implementation, so treat sample records from your own run as the source of truth. Records can be reviewed in Apify and exported to JSON or CSV. Do not build a production integration around an assumed field until it has been observed in representative results.

Limitations and responsible use

The actor does not guarantee complete coverage of InfoJobs, a fixed number of results, real-time freshness, stable fields, or uninterrupted execution. Results can vary with query wording, search availability, pagination, response changes, access conditions, proxy behavior, and source-side restrictions. A failed or partial run should be reviewed rather than interpreted as proof that no matching jobs exist.

Use only data and access methods you are permitted to use. Respect InfoJobs terms, applicable laws, privacy requirements, rate limits, and any restrictions on storing or redistributing job or personal information. This actor is a collection tool, not a guarantee that a listing is current, genuine, available, or suitable for a hiring decision.

Quick-start example

Review the current schema, provide a focused query or InfoJobs search URL, set maxItems to a conservative test value, and run the actor. Inspect the resulting dataset before scaling the run or automating an export.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.