The fastest way to collect job listings from several job boards is to use one multi-source Actor that returns a shared schema. That removes most of the adapter work, but it does not remove the need to define sources, locations, freshness, duplicate rules, and required fields before the run.
All Jobs Scraper (39 Sites) is the AgentX starting point when one query must cover multiple global and regional job sources. Use a focused source Actor instead when one board-specific field or filter matters more than cross-source consistency.
When does a multi-source job scraper make sense?
Use a multi-source scraper when the output must be compared, merged, or delivered through one downstream contract. Common examples include competitor hiring monitors, labor-market dashboards, talent-supply research, lead generation, and internal job-search products.
A multi-source run is especially useful when the workflow needs to answer questions such as:
- Which employers are adding the most roles in a market?
- Which locations are gaining remote or hybrid openings?
- Which skills appear across competing job boards?
- How often does the same vacancy appear on several sources?
- Which companies are recruiting for a new function or geography?
If the question is limited to one source—for example, “return Glassdoor salary context with each listing”—start with the dedicated Glassdoor Jobs Scraper and validate its current output contract.
What should the normalized job schema contain?
Define the minimum usable row before collecting anything. A durable job record usually contains:
| Field group | Minimum useful fields | Why it matters |
|---|---|---|
| Identity | title, company, source URL, source ID | Supports traceability and duplicate detection |
| Location | city, region, country, remote status | Enables geographic comparison |
| Employment | job type, seniority, workplace type | Separates unlike roles |
| Timing | posted date, collected time | Supports freshness and trend analysis |
| Compensation | salary range, currency, period | Allows comparison only when units are explicit |
| Content | description, requirements, skills | Powers search, classification, and enrichment |
Do not silently convert missing values into false certainty. A blank salary is different from a confirmed “salary not offered,” and a missing remote flag is different from an on-site requirement.
How should duplicate jobs be handled?
Keep both a source-level record and a cross-source grouping key. Deleting duplicates too early destroys useful evidence about distribution and source coverage.
A practical grouping key can combine a normalized employer name, normalized title, location, and a fingerprint of the description. Exact source URLs remain attached to every record. The grouped entity then records:
- the first and last observed date;
- every source where the job appeared;
- field disagreements between sources;
- the most complete description;
- the confidence that the records describe the same opening.
This produces a cleaner analytical table without pretending that two similar listings are always identical.
What is the recommended collection workflow?
- Write the query contract. Specify keywords, locations, countries, remote handling, recency, and result limits.
- Run a small sample. Collect enough records to expose field and source differences without paying for a full crawl.
- Inspect source coverage. Verify that each requested source returned data and record why a source may be empty.
- Profile the schema. Count missing values, types, currencies, date formats, and unusually long descriptions.
- Create duplicate groups. Preserve raw rows while building an analytical view.
- Export to the consumer. Apify datasets can be retrieved through the API or exported in formats including JSON, JSONL, CSV, XML, Excel, HTML, and RSS, according to the current dataset documentation.
- Schedule only after validation. A repeated bad query creates more bad data, not more insight.
How do you verify a real run?
Check at least five things before scaling:
- Every row has a working source URL.
- Dates are parseable and the timezone assumption is documented.
- Country and currency are explicit where compensation exists.
- The same query produces a plausible source distribution.
- Required downstream fields are present often enough to support the intended analysis.
The Apify page is the source of truth for current inputs, outputs, pricing, and examples. BestCrawler’s complete Actor catalog helps find alternatives, but the live contract should decide the production dependency.
Which Actor should you choose next?
Start with All Jobs Scraper for one standardized dataset across many sources. Compare it with LinkedIn Jobs Scraper, Indeed Jobs Scraper, or Glassdoor Jobs Scraper when source-specific behavior is central to the research question.
For the decision framework behind that comparison, read How to Choose an Apify Actor for a Production Data Workflow.
Continue in the directory
Turn the guide into a real sample run.
Open the current AgentX contract, check pricing and fields, then validate a narrow output.