LinkedIn Job Scraper

LinkedIn Job Scraper creates a structured dataset of job postings collected from LinkedIn. Each dataset item represents one LinkedIn job and can include identifiers, title, description, posting dates, employment type, company name and URL, location fields, coordinates, industry, skills, experience, and education. Query the source with keyword searches (searchQueries) plus locations and filters, or with LinkedIn job search URLs in startUrls (these override searchQueries when provided). Control the result limit with maxItems, and retrieve records through the Apify Dataset API or export them as JSON, CSV, Excel, XML, or another supported format.

Dataset at a glance

Property	Value
Source	linkedin.com (public job listings)
Record unit	One LinkedIn job
Input methods	Keyword search (searchQueries) plus locations and filters, or LinkedIn job search URLs (startUrls)
Main identifiers	jobId, url
Delivery	Apify Dataset and API
Export formats	JSON, CSV, Excel, XML, HTML (Apify dataset exports)
Update model	Fresh records per Actor run
Pricing	$0.00005 per Actor start; $0.001 per job ($1 per 1,000 jobs)

Coverage and available records

The Actor collects public LinkedIn job listings using one of two entry points:

• Search-based: Set searchQueries to job titles or keywords (for example Python Developer). Each query is a separate search. locations applies to every query. Filters (timeRange, jobType, experienceLevel, remote) narrow the search.
• URL-based: Pass LinkedIn job search URLs in startUrls. When startUrls is provided, searchQueries is ignored.

Record types and limits:

• Job records are always collected: jobId, title, description, dates, employment type, company name and URL, location fields, coordinates, industry, skills, experience, education, and the job URL.
• Company page fields are conditional: they attach onto the same job record only when extractCompanyDetails is enabled. They are not a separate dataset item.
• Result cap: maxItems limits the number of jobs collected (0 means unlimited, the default; Console prefill is 10).

Known exclusions: content LinkedIn only shows behind login is not collected; each run captures listing state at run time (no historical snapshots); company page fields are omitted when extractCompanyDetails is off or the company page is unavailable.

Data dictionary

Field names below match dataset record JSON properties exactly. jobId is the recommended deduplication key; url is a reasonable secondary key. Dates are ISO 8601 timestamps as returned on the listing (for example 2026-05-08T13:38:46.000Z). country is a country code. latitude and longitude are decimal degrees when present.

Field	Type	Nullable	Description	Example
jobId	string	No	LinkedIn job identifier; best stable deduplication key	4412111373
title	string	No	Job title	Python Developer
description	string	Yes	Full job description text	See example record
datePosted	string	Yes	Posting timestamp (ISO 8601)	2026-05-08T13:38:46.000Z
validThrough	string	Yes	Listing end timestamp (ISO 8601)	2026-11-04T13:38:46.000Z
employmentType	string	Yes	Employment type as shown on the listing	FULL_TIME
companyName	string	Yes	Hiring company name	DATAGROUP
companyUrl	string	Yes	LinkedIn company page URL	https://de.linkedin.com/company/datagroup-se
companyLogo	string	Yes	Company logo image URL	See example record
country	string	Yes	Country code	DE
city	string	Yes	City as shown on the listing	Krakau
latitude	number	Yes	Location latitude in decimal degrees	51.36391
longitude	number	Yes	Location longitude in decimal degrees	11.857489
industry	string	Yes	Industry text as shown on the listing	IT-Dienstleistungen und IT-Beratung
skills	string	Yes	Skills text; empty when the listing does not provide it	""
monthsOfExperience	string	Yes	Experience requirement as returned on the listing; empty when not provided	""
educationLevel	string	Yes	Education requirement as shown on the listing	bachelor degree
url	string	No	Job posting URL; secondary deduplication key	https://de.linkedin.com/jobs/view/python-developer-at-datagroup-4412111373
location	string	Yes	Full location string (city, region, country)	Krakau, Saxony-Anhalt, Germany

When extractCompanyDetails is true, company page fields attach onto this same job record. They are omitted when the toggle is off.

Example dataset record

Real record produced with the test input below (searchQueries: Python Developer, locations: Germany, maxItems: 10). The description text is shortened for readability; the live record contains the full listing copy.

{
  "jobId": "4412111373",
  "title": "Python Developer",
  "description": "Job Description DATAGROUP Polska. Design and implement robust, scalable, and efficient JAVA-based frontend as well as Python components for the ETL process.",
  "datePosted": "2026-05-08T13:38:46.000Z",
  "validThrough": "2026-11-04T13:38:46.000Z",
  "employmentType": "FULL_TIME",
  "companyName": "DATAGROUP",
  "companyUrl": "https://de.linkedin.com/company/datagroup-se",
  "companyLogo": "https://media.licdn.com/dms/image/v2/C4D0BAQFCZT1emBoUsw/company-logo_200_200/company-logo_200_200/0/1630477221838/datagroup_ag_logo?e=2147483647&v=beta&t=kHRk6yObQhtWun4zVyZKfRI6iNwfH5xZ2aTfFnDTOjQ",
  "country": "DE",
  "city": "Krakau",
  "latitude": 51.36391,
  "longitude": 11.857489,
  "industry": "IT-Dienstleistungen und IT-Beratung",
  "skills": "",
  "monthsOfExperience": "",
  "educationLevel": "bachelor degree",
  "url": "https://de.linkedin.com/jobs/view/python-developer-at-datagroup-4412111373",
  "location": "Krakau, Saxony-Anhalt, Germany"
}

The record above was produced with this input:

{
  "searchQueries": ["Python Developer"],
  "locations": ["Germany"],
  "timeRange": "any",
  "maxItems": 10,
  "extractCompanyDetails": false,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Query and input reference

Input	Type	Required	Default	Accepted values	Description
searchQueries	array	No*	[]	Free-text keywords (prefill Python Developer)	Job titles or keywords. Each value is a separate search. Ignored when startUrls is provided
startUrls	array (requestListSources)	No*	[]	LinkedIn job search URLs	URL-based entry point. Overrides searchQueries when provided
locations	array	No	[]	Free-text locations (e.g. Germany, London)	Location filter applied to every search query
timeRange	string	No	any	any, past24Hours, pastWeek, pastMonth	Filter jobs by posting date
jobType	array	No	[]	fullTime, partTime, contract, temporary, internship, volunteer	Employment-type filter; empty means all types
experienceLevel	array	No	[]	internship, entryLevel, associate, midSenior, director, executive	Experience-level filter; empty means all levels
remote	array	No	[]	onSite, remote, hybrid	Work-arrangement filter; empty means all types
maxItems	integer	No	0	0 or any positive integer	Maximum jobs to collect; 0 = unlimited (Console prefill 10)
extractCompanyDetails	boolean	No	false	true / false	Attach company page fields onto each job record
maxConcurrency	integer	No	5	1–20	Maximum parallel detail-page fetches
proxyConfiguration	object	No	Apify proxy, RESIDENTIAL group	Apify proxy groups or custom proxies	Residential proxies are recommended

* Provide searchQueries or startUrls — at least one source is required to collect records.

Minimal request:

{ "searchQueries": ["Python Developer"], "locations": ["Germany"] }

Advanced request (filters plus company page fields):

{
  "searchQueries": ["Python Developer"],
  "locations": ["Germany"],
  "timeRange": "pastWeek",
  "jobType": ["fullTime"],
  "experienceLevel": ["midSenior"],
  "remote": ["remote", "hybrid"],
  "maxItems": 500,
  "extractCompanyDetails": true,
  "maxConcurrency": 5,
  "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }
}

Retrieve the data through the API

The Actor runs on the Apify platform, so there is no server to host and no crawling infrastructure to maintain.

1. Start the Actor with a JSON input (console or API).
2. Wait for the run to finish, or use a synchronous endpoint if you want the response inline.
3. Retrieve items from the run's default dataset.
4. Paginate or export the dataset.

Python example:

from apify_client import ApifyClient

client = ApifyClient("YOUR-APIFY-TOKEN")

run_input = {
    "searchQueries": ["Python Developer"],
    "locations": ["Germany"],
    "maxItems": 10,
}

run = client.actor("datascrapers/linkedin-job-scraper").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["jobId"], item["title"], item["companyName"])

Apify generates ready-to-run Python, JavaScript, and cURL examples on the Actor's API tab. Do not put a real API token in shared code or URLs.

Data quality and record handling

• Conditional fields: company page fields are present only when extractCompanyDetails is enabled. Listings alone return the job fields in the data dictionary.
• Source changes: LinkedIn page structure and values can change; fields the listing does not provide are returned as empty values rather than fabricated.
• Deduplication: within a run, repeated jobId values are skipped. Across runs, the Actor does not deduplicate. Use jobId as the stable key, or url as a secondary key, and filter repeated runs against previously stored identifiers.
• Empty source values: skills and monthsOfExperience are often empty strings when the listing does not publish them. latitude and longitude are omitted or empty when coordinates are not present.
• Normalization: dates are stored as ISO 8601 strings; country is a country code; employmentType uses the listing value (for example FULL_TIME). Values are read from the listing and mapped to the field types in the data dictionary.

Export and pipeline examples

Destination	Recommended method	Typical use
PostgreSQL / Supabase	Dataset API poll or webhook consumer	Store job rows for labor-market or recruiting analysis
Google Sheets	Apify Google Sheets integration	Share shortlists with recruiting or research teams
ATS / CRM	Webhook on run completion	Push new jobs into sourcing pipelines
S3 / cloud storage	Scheduled export via Apify scheduler + integration	Archival of hiring-market snapshots

Pricing and cost examples

The Actor uses pay-per-event pricing. Live charges are:

Event	Trigger	Rate
Actor start	Once per run	$0.00005
Job result (apify-default-dataset-item)	Every job record written to the default dataset	$0.001 ($1 per 1,000 jobs)

extractCompanyDetails attaches company page fields onto the same job record. It is not a separate billed event.

Example costs:

Records	Configuration	Estimated base cost
1,000	Jobs only	$1.00005
10,000	Jobs only	$10.00005
1,000	Jobs + company page fields	$1.00005
10,000	Jobs + company page fields	$10.00005

Compute units and proxy traffic consumed by the run are billed by your Apify plan. Estimates depend on the verified pricing model and the options selected for the run.

Limitations and responsible data use

• The Actor collects publicly accessible data from LinkedIn job pages only.
• Field availability depends on what LinkedIn renders at run time; some values can be empty or missing, and site changes can alter fields.
• Company page fields attach onto job records; they are not standalone dataset items.
• The Actor does not provide historical snapshots unless you store them yourself.
• Residential proxies are recommended; coverage may degrade without them.
• You are responsible for compliance with LinkedIn's terms of service, applicable privacy law, and any contractual obligations before using the data.

Dataset questions

What does one dataset item represent?

One LinkedIn job. Company page fields attach onto that same row when extractCompanyDetails is enabled. Enabling company details does not create extra dataset items.

Which field should I use as a unique identifier?

jobId is the LinkedIn job identifier and is the recommended deduplication key. url is a reasonable secondary key.

Are fields nullable or conditional?

Yes. Company page fields exist only when extractCompanyDetails is enabled. Within job records, fields the listing does not publish (commonly skills and monthsOfExperience) are returned as empty values.

Do startUrls override searchQueries?

Yes. When startUrls contains LinkedIn job search URLs, those URLs are used and searchQueries is ignored. Use searchQueries plus locations and filters when you want keyword search.

Can I retrieve the records as CSV or JSON?

Yes. The dataset can be exported as JSON, CSV, Excel, XML, or HTML from the Apify Console, and queried through the Dataset API.

Does the Actor return historical data?

No. Each run captures the state of the listings at run time. To track hiring changes over time, schedule repeated runs and store the outputs yourself.

What counts as a billable result?

Two charges apply: $0.00005 once per Actor start, and $0.001 for every job record written to the default dataset ($1 per 1,000 jobs). Company page fields do not add a billed event.

Related datasets from Data Scrapers

• Dice.com Job Scraper — Technology job listings that can be compared with LinkedIn jobs by title, company, and skills.
• Naukri Job Scraper — India job listings that can be joined with LinkedIn rows by title and company for cross-board coverage.
• Glassdoor Jobs Scraper — Job listings with employer ratings that can be joined to LinkedIn jobs by company name and title.
• ZipRecruiter Job Scraper — US job listings for labor-market comparison against LinkedIn roles.
• LinkedIn Company Scraper — Company profiles that can be joined with these job records by company name or companyUrl.
• LinkedIn Profile Scraper — Public profile records for hiring-team or candidate research alongside job listings.

Data Scrapers support

Need an additional field, record type, or export workflow? Contact Data Scrapers at stardustspotlight@gmail.com. Include a sample source URL, required fields, expected record volume, and preferred delivery format.