Skip to main content

​
Overview

The LinkedIn Webset provides enriched profile data for active LinkedIn users, combining raw LinkedIn data with additional fields: GitHub profile mappings (for ~5M users) and personal emails.
Dataset Size: 700M+ LinkedIn profiles

​
What’s Included

The LinkedIn Webset enriches standard LinkedIn profile data with:

​
Professional Information

  • Current position - Job title, company, start date, and location
  • Experience history - Complete work history with company details and locations
  • Education - Degrees, majors, minors, and schools attended
  • Skills & endorsements - Professional skills and competencies
  • Certifications - Professional certifications with credential IDs and issuers

​
Personal Data

  • Name information - Full name, first, middle, and last name
  • Contact information - Personal emails (when available)
  • Location data - Structured location (city, state, country, continent)
  • Languages - Languages spoken and proficiency levels
  • Social metrics - Connections, followers, and following counts

​
GitHub Mapping

  • GitHub profiles - Connected GitHub accounts for ~5M LinkedIn users
  • Technical activity - Repositories, commits, stars, and contribution history
  • Technical profile - Languages, frameworks, and open source involvement

​
Common Use Cases

B2B Sales & Prospecting

Identify decision-makers and key contacts at target companies. Enrich leads with professional history, company info, and verified contact details.

Recruiting & Headhunting

Find candidates with specific experience, skills, or educational backgrounds. Use must_have=experience to only get complete professional profiles.

Market Research

Analyze industry trends, company movements, and talent flows. Research competitors’ teams and organizational structures.

Technical Recruiting

Combine LinkedIn professional data with GitHub technical profiles using addons=github to find developers with both proven experience and active code contributions.

​
API Reference

For complete API documentation including endpoints, parameters, authentication, and response schemas:

View LinkedIn Webset API Reference

Complete request/response documentation, authentication details, and code examples

​
Quick Start Example

Here’s a minimal example to get started: 
curl -X GET "https://api.peoplecontext.com/v1/webset/linkedin/person?linkedin=taylor-jordan-lee&addons=github,personal_email" \
  -H "x-api-key: YOUR_API_KEY"

​
Key Features

​
GitHub Addon

Add addons=github to include comprehensive GitHub profile data in the response. This is particularly powerful for technical roles, providing:
  • Repository ownership and contribution history
  • Programming languages and technologies used
  • Open source activity and community engagement
  • Stars and interests in technical topics

​
Personal Email Addon

Add addons=personal_email to include personal email addresses when available. This provides additional contact methods beyond professional emails.

​
Must-Have Fields

Use the must_have parameter to only pay for profiles that meet your requirements:
  • must_have=experience - Only return profiles with work experience
  • must_have=location,education - Must have location OR education
  • must_have=current_position - Only return profiles with current employment
When a profile doesn’t meet your requirements, an empty object {} is returned and you are not charged.

​
Rich Professional Context

Every profile includes detailed career information:
  • Complete work history with company names, roles, and dates
  • Education background with degrees, schools, and fields of study
  • Professional skills and areas of expertise
  • Certifications demonstrating qualifications

​
Best Practices

Optimize costs with must_have: Only pay for profiles that meet your requirements by specifying required fields like must_have=experience.
Rate limiting: API requests are subject to rate limits based on your plan. Contact support if you need higher limits.
Data freshness: LinkedIn data is refreshed as frequently as possible from our data provider. The last_updated field indicates when the profile was last enriched.

​
Response Structure

Here’s what a typical response looks like (trimmed for clarity): 
{
  "first_name": "Taylor",
  "last_name": "Lee",
  "full_name": "Taylor Jordan Lee",
  "headline": "Senior Product Manager at Nimbus Analytics",
  "location": {
    "city": "London",
    "country": "United Kingdom"
  },
  "current_position": {
    "summary": "Senior Product Manager at Nimbus Analytics",
    "start": "2022-04-01",
    "company": {
      "name": "Nimbus Analytics",
      "linkedin_url": "https://www.linkedin.com/company/nimbus-analytics"
    }
  },
  "experience": [
    {
      "summary": "Senior Product Manager at Nimbus Analytics",
      "start": "2022-04-01",
      "current": true,
      "company": { "name": "Nimbus Analytics" }
    }
  ],
  "education": [
    {
      "summary": "MSc in Data Science",
      "degrees": ["MSc"],
      "majors": ["Data Science"],
      "school": { "name": "University of Edinburgh" }
    }
  ],
  "skills": [
    { "skill": "Product Management" },
    { "skill": "Analytics" }
  ],
  "personal_email": {
    "email": "[email protected]"
  },
  "github": {
    "username": "taylor-lee",
    "repos": [
      {
        "full_name": "taylor-lee/feature-flags-service",
        "language": "Go",
        "description": "Simple feature flag service"
      }
    ]
  }
}
For the complete schema, see the API Reference.