LinkedIn Job Scraper

An Apify actor that scrapes and processes job listings from LinkedIn. It extracts detailed job data, cleans the information, and structures it for analysis or integration.

Key Features

• Direct LinkedIn Scraping: Fetches job listings directly from LinkedIn's search.
• Smart Data Processing: Automatically extracts and structures key information from job descriptions.
• Flexible Configuration: Control the search with parameters for keywords, location, job type, and more.
• Data Enhancement: Cleans raw text, standardizes formats, and enriches data with derived fields.
• Apify-Optimized: Built for deployment and scaling on the Apify platform with robust error handling.

How to Use

Configure the actor using input parameters. Here's the basic structure:

Required Parameters

• search_term (string): Job title or keywords. Default: "python developer".
• location (string): Geographic location. Default: "United States".

Common Optional Parameters

• results_wanted (integer): Number of jobs to fetch (1-1000). Default: 50.
• hours_old (integer): Max age of postings in hours. Default: 168 (7 days).
• job_type (string): Filter by "fulltime", "parttime", "contract", or "internship".
• is_remote (boolean): Set to true for remote-only jobs.
• easy_apply (boolean): Set to true for jobs with LinkedIn's Easy Apply.

Example Input (JSON)

{
  "search_term": "data scientist",
  "location": "San Francisco, CA",
  "results_wanted": 100,
  "hours_old": 72
}

Input & Output

Input Parameters Summary

The scraper is controlled via the parameters listed above. Key filters include job_type, is_remote, easy_apply, and distance (search radius in miles).

Output Data

Each job result includes these structured fields:

• Basic Info: company, title, location, date_posted, site.
• URLs: job_url, job_url_direct, company_url, company_logo.
• Job Details: description (cleaned), salary, job_level, job_function, work_type.
• Enhanced Data: skills (extracted list), experience_range, id (unique identifier).

Smart Processing Details

• Skill Extraction: Identifies technologies like Python, React, AWS, SQL, Docker from descriptions.
• Experience Detection: Categorizes requirements (e.g., Entry level, 2-5 years, Senior).
• Salary Parsing: Extracts salary data from text and structured fields, handling multiple currencies and formats.
• Data Cleaning: Removes markdown, fixes URLs, and standardizes text.

Performance & Troubleshooting Tips

• Start with a low results_wanted value for testing.
• Use the hours_old filter to get recent postings.
• Specific locations yield better results than broad ones.
• If you get no results, check your search_term and location spelling, or increase hours_old.