LinkedIn Profile Extractor and B2B Email Enricher

Our powerful tool allows you to gather deep, structured data from LinkedIn profiles at lightning speed. You can collect complete data from the experience, education, skills, and certifications sections of any public profile without triggering platform restrictions by leveraging an array of secure, rotating session cookies.

Optionally, our tool features a state-of-the-art Multi-Method Email Discovery engine. This is ideal for lead generation, recruitment, and high-level networking. We perform comprehensive validation checks, including native profile regex scanning, internal contact info harvesting, and advanced SMTP/MX probing to ensure the email addresses are valid and deliverable. Adaptive run-time: if you toggle the email search off, the scraper flies through profiles almost instantly.

Key Benefits

• Cost-Efficient Toggle: Run pure data extraction at maximum speed, or toggle on the B2B Email Enricher only when you need deep prospecting.
• Fast response times & multi-threaded concurrency.
• Real-time scraping - fresh data bypassing local caching.

How It Works

Simply provide one of the following:

• List of LinkedIn profile URLs (e.g., https://www.linkedin.com/in/demishassabis)
• List of LinkedIn public identifiers (e.g., demishassabis)

Email Search (Toggle) By selecting the "Enrich Email (B2B)" mode in your Input configuration, you can enable our multi-layer pipeline to discover hidden B2B emails. The scraper will intelligently extract explicit emails. Important Note: While our tool employs 5 separate fallback layers to guarantee the highest possible email discovery probability, no tool can extract an email for 100% of profiles if the data does not exist or the corporate firewall rigidly drops all traffic.

Data You'll Receive

• Profile summary, headline, and total follower counts
• Current and previous work experience (with complete descriptions)
• Educational qualifications
• Certifications, Languages, and Volunteer experiences
• Top skills extracted directly from the profile
• Built-in email and emailConfidence scoring (e.g., contact_info, smtp_accepted, pattern_guess, no_mx)

Sample Output Data

Here is a sample output showing the exact schema of the extracted data:

[
  {
    "publicIdentifier": "demishassabis",
    "profileUrl": "https://www.linkedin.com/in/demishassabis",
    "fullName": "Demis Hassabis",
    "firstName": "Demis",
    "lastName": "Hassabis",
    "headline": "Co-Founder & CEO, Google DeepMind",
    "summary": "Co-Founder & CEO of Google DeepMind - working on AGI, responsible for AI breakthroughs such as AlphaGo, the first program to beat the world champion at the game of Go; and AlphaFold, which cracked the 50-year grand challenge of protein structure prediction and was recognised with the 2024 Nobel Prize in Chemistry. Revolutionising drug discovery at Isomorphic Labs. Ultimately trying to understand the fundamental nature of reality.",
    "location": "United Kingdom",
    "industryName": "",
    "profilePicture": "https://media.licdn.com/dms/image/v2/D4E03AQHDlCnuxugKuA/profile-displayphoto-shrink_100_100/B4EZSKW58BHAAU-/0/1737488048398?e=1781136000&v=beta&t=hHpxLWJ9hhYLM-k-aTwZGFcC7fe7nbNk1mYsuUXeUpk",
    "backgroundImage": "https://media.licdn.com/dms/image/v2/D4D16AQGKHrdjpqvDCA/profile-displaybackgroundimage-shrink_350_1400/profile-displaybackgroundimage-shrink_350_1400/0/1715180022856?e=1781136000&v=beta&t=_mN12QnUMv7MOZTMsAQ7QaLvl-Vom8lDpoIXaesyOZk",
    "connectionCount": null,
    "followerCount": null,
    "email": "demis.hassabis@google.com",
    "emailConfidence": "contact_info",
    "experience": [
      {
        "title": "Co-Founder & CEO",
        "companyName": "Google DeepMind",
        "companyLinkedInUrl": "https://www.linkedin.com/company/1594050",
        "location": null,
        "startDate": "2010",
        "endDate": null,
        "duration": null,
        "description": null
      },
      {
        "title": "Founder & CEO",
        "companyName": "Isomorphic Labs",
        "companyLinkedInUrl": "https://www.linkedin.com/company/82385765",
        "location": null,
        "startDate": "2021",
        "endDate": null,
        "duration": null,
        "description": null
      },
      {
        "title": "Research Fellow",
        "companyName": "Gatsby Computational Neuroscience Unit, UCL",
        "companyLinkedInUrl": "https://www.linkedin.com/company/4171",
        "location": null,
        "startDate": "2009",
        "endDate": "2010",
        "duration": null,
        "description": null
      },
      {
        "title": "Early Gaming Career",
        "companyName": "Elixir Studios, Lionhead Studios, Bullfrog Productions",
        "companyLinkedInUrl": null,
        "location": null,
        "startDate": "1992",
        "endDate": "2005",
        "duration": null,
        "description": "Worked on the AI and design for pioneering simulation games such as Theme Park (1994), Black & White (2001), and Republic: the Revolution (2003)"
      }
    ],
    "education": [
      {
        "schoolName": "University College London, U. of London",
        "schoolLinkedInUrl": "https://www.linkedin.com/school/12682",
        "degree": "PhD",
        "fieldOfStudy": "Cognitive Neuroscience",
        "startDate": null,
        "endDate": null,
        "description": null
      },
      {
        "schoolName": "University of Cambridge",
        "schoolLinkedInUrl": "https://www.linkedin.com/school/12691",
        "degree": "BA",
        "fieldOfStudy": "Computer Science",
        "startDate": null,
        "endDate": null,
        "description": null
      }
    ],
    "skills": [
      {
        "name": "Mathematical Modeling"
      },
      {
        "name": "Algorithms"
      },
      {
        "name": "Video Games"
      },
      {
        "name": "Cognitive Neuroscience"
      },
      {
        "name": "Statistics"
      },
      {
        "name": "Computer Vision"
      },
      {
        "name": "Game Development"
      },
      {
        "name": "C++"
      },
      {
        "name": "Optimization"
      },
      {
        "name": "Computer Science"
      },
      {
        "name": "Game Design"
      },
      {
        "name": "Applied Mathematics"
      },
      {
        "name": "Artificial Intelligence"
      },
      {
        "name": "Matlab"
      },
      {
        "name": "Pattern Recognition"
      },
      {
        "name": "Programming"
      },
      {
        "name": "Image Processing"
      },
      {
        "name": "Machine Learning"
      },
      {
        "name": "Software Engineering"
      },
      {
        "name": "Computer Games"
      }
    ],
    "languages": [],
    "certifications": [],
    "volunteerExperiences": [],
    "scrapedAt": "2026-05-26T04:53:55.121Z"
  }
]

Exporting and API

As a standard Apify Actor, the tool automatically stores results in a dataset. You can easily export this data in various formats such as CSV, JSON, XLS, etc., or access the data on-demand via the Apify API.

Support and Feedback

We continuously enhance our tools based on user feedback. If you encounter technical issues or have suggestions for improvement, please open an Issue in the Apify Console.

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Disclaimer: This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedIn® is a registered trademark of LinkedIn Corporation. All trademarks are property of their respective owners. Please ensure your usage complies with applicable data privacy laws (e.g., GDPR, CCPA).