LinkedIn Profile Scraper: Extract Professional Data from LinkedIn at Scale

Extracting structured data from LinkedIn profiles can be a game-changer for recruiters, sales teams, and researchers. This LinkedIn profile scraper leverages Puppeteer's headless browser technology to pull publicly available information and return it in clean JSON format—perfect for building lead databases, enriching CRM systems, or powering recruitment workflows. With proper setup, you can automate data collection while maintaining reliability and speed, even when running on remote servers.

What Data Can You Extract from LinkedIn Profiles?

This scraper targets publicly visible information across multiple profile sections:

Profile basics include full name, current job title, location details, profile photo URL, bio description, and the direct profile link.

Work experience captures job titles, company names, employment locations, duration calculations, precise start and end dates, and role descriptions—all formatted consistently.

Educational background pulls school names, degree types, fields of study, and attendance dates with automatic duration calculations.

Volunteer work documents organization names, volunteer roles, activity descriptions, and service periods.

Skills and endorsements list each skill alongside its endorsement count, helping you gauge expertise levels.

All date fields follow a standardized format, making the data immediately usable for database imports or analytics pipelines.

Setting Up Your LinkedIn Scraper

Getting started requires a valid LinkedIn session rather than traditional username-password authentication. This approach bypasses common security obstacles like CAPTCHA challenges and location-based login blocks that typically occur when automating from servers.

Create a dedicated LinkedIn account with privacy settings enabled to prevent profile view notifications. Log into this account through your regular browser, then open Developer Tools to locate the cookie named li_at. This session cookie value becomes your sessionCookieValue parameter when initializing the scraper.

Install the package with npm install linkedin-profile-scraper and you're ready to start extracting data.

How Session-Based Authentication Works Better

Traditional email-password automation often triggers LinkedIn's security systems, especially when requests originate from cloud servers or unfamiliar IP addresses. Session cookies circumvent these issues because they represent an already-authenticated browser session.

This method proves more reliable for server-side deployments where consistent access matters more than convenience. When scraping at scale, dealing with location-based blocks and CAPTCHA solving quickly becomes impractical.

If you're building commercial scrapers that need rock-solid reliability across thousands of profiles, consider professional solutions. 👉 ScraperAPI handles session management, proxy rotation, and CAPTCHA solving automatically, letting you focus on using the data rather than maintaining the infrastructure.

Performance Optimization and Memory Management

The scraper offers a keepAlive configuration that maintains Puppeteer in the background between scrapes. When enabled, Chromium stays loaded in memory (roughly 75MB idle), dramatically reducing startup time for subsequent requests. This works well for batch processing or real-time applications.

For occasional scraping needs, the default behavior closes the browser after each successful extraction, freeing system resources immediately. Choose based on your usage pattern—continuous processing favors keepAlive, while intermittent jobs benefit from the default cleanup.

Typical scraping takes several seconds per profile as the script scrolls through sections and expands collapsed content to ensure complete data capture.

Handling Session Expiration

LinkedIn sessions eventually expire with inactivity, causing the scraper to fail authentication. The tool specifically detects and reports this condition so you can refresh your credentials.

When you receive a session expiration error, simply grab a fresh li_at cookie value from LinkedIn.com using the same browser inspection method. Update your sessionCookieValue parameter and resume operations. This maintenance step typically occurs after extended periods without LinkedIn activity.

Example Data Structure

The scraper returns deeply nested JSON with consistent field naming. Here's a condensed view of the output format:

The userProfile object contains fullName, title, location (with city, province, country), photo URL, description, and profile url.

Each experiences entry includes title, company, employmentType, location, startDate, endDate, endDateIsPresent boolean, description, and calculated durationInDays.

Education records show schoolName, degreeName, fieldOfStudy, date ranges, and duration calculations.

Skills appear as objects with skillName and endorsementCount integers.

This structured format integrates smoothly with databases, spreadsheets, or downstream processing pipelines without additional parsing.

Technical Considerations

The headless browser approach means you're essentially running a real Chrome instance. This provides excellent compatibility with modern web applications but requires adequate system resources. Each browser instance consumes memory and CPU during active scraping.

For high-volume operations, you'll need to manage concurrent browser sessions carefully to avoid overwhelming your server. Rate limiting becomes essential to respect LinkedIn's systems and maintain account health.

Session management remains your responsibility—expired cookies stop all scraping until refreshed. Building monitoring around authentication status helps catch issues before they disrupt workflows.

Conclusion

LinkedIn profile scraping transforms scattered professional information into actionable structured data for recruitment, sales intelligence, and market research. This Puppeteer-based scraper handles the complexity of modern web scraping while returning clean JSON ready for immediate use. When scaling beyond basic automation or managing hundreds of profiles, proven infrastructure solutions eliminate the operational overhead. For production deployments requiring consistent uptime and compliance with LinkedIn's public data access, ScraperAPI provides enterprise-grade reliability with built-in session management and automatic retry logic—check out their 👉 professional scraping infrastructure designed for LinkedIn and beyond.