LinkedIn Job Scraper

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Node Inputs

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Company Identification (At least one required)

• Company Domain: Company’s website domain (e.g., google.com)
• Company Name: Official company name (e.g., Google)

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Search Parameters (Optional)

• Keyword: Search term for job titles or descriptions (e.g., “software engineer”)
• Job Type: Filter by employment type:
  • Full-time
  • Part-time
  • Contract
  • Internship
  • Temporary
  • Volunteer
  • Anything (default)
• Experience Level: Filter by seniority:
  • Internship
  • Entry_level
  • Associate
  • Mid_senior_level
  • Director
  • Anything (default)
• Time Posted: Filter by posting date:
  • Yesterday
  • Past-week
  • Past-month
  • Anytime (default)
• Workplace Type: Filter by work location:
  • Remote
  • On-site
  • Hybrid
  • Anything (default)
• Maximum Number of Jobs: Limit number of results (default: 10)

Note: Use the ‘Configure Inputs’ option to expose these fields as inputs to the node. This is particularly useful for Loop Mode operations.

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Node Output

• Total Company Listings: Total number of active job postings for the company

Note: This is different from the “Maximum Number of Jobs” parameter, which limits how many jobs you want to retrieve. For instance, even if a company has 500 total listings, you might only want to fetch the 10 most recent ones.

• Posted Date: When each job was posted
• Location: Job locations
• Company URL: LinkedIn company profile URLs
• Company Name: Company names
• Job URL: Direct links to job listings
• Job Title: Position titles

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Node Functionality

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Key Features

• Multiple company identification methods
• Comprehensive search filters
• Customizable information selection
• No rate limits
• Loop Mode support for multiple companies

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Example Use Cases

1. Tech Talent Market Analysis

LinkedIn Job Scraper → Website Scraper → Ask AI → Slack Block Kit Sender

• Use Job Scraper to collect job postings from top tech companies with “software engineer” keyword
• Website scraper node scrapes the job listing URL
• Ask AI analyzes the scraped data to identify:
  • Most in-demand programming languages
  • Common experience requirements
  • Emerging technical skills
• Slack Message Sender delivers weekly trend reports in formatted blocks

2. Competitive Hiring Intelligence

LinkedIn Job Scraper → Categorizer → Notion Page Writer

• Job Scraper monitors competitors’ job posts with director-level filter enabled
• Categorizer automatically classifies jobs into departments:
  • Engineering & Tech
  • Sales & Marketing
  • Product & Design
  • Operations & Finance
• Notion pages are organized by department with automated updates about:
  • New leadership positions
  • Team expansion areas
  • Required qualifications

3. Job Market Dashboard

LinkedIn Job Scraper → Website Scraper → Ask AI → Google Sheets Writer

• Job Scraper retrieves latest job posts from target companies with experience_level filter
• Website scraper node scrapes the job listing URL
• Ask AI processes job descriptions to extract:
  • Salary ranges when mentioned
  • Required qualifications
  • Benefits packages
• Google Sheets Writer updates a live dashboard with:
  • Job count by level
  • Compensation trends
  • Skills in demand

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Important Considerations

1. Credits Usage
   • 27 credits per scrape
   • Reduced to 3 credits with own Proxycurl API key
2. Data Availability
   • Results depend on public job listings
   • Some fields may be empty if information isn’t available
3. API Limits
   • No rate limiting with Proxycurl integration
   • Suitable for bulk processing
4. Search Optimization
   • More specific searches yield better results
   • Combine multiple filters for precision
   • Use company domain for accurate targeting