We extract public profiles, company pages, job postings, and alumni distributions from LinkedIn. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Public Profiles objects from linkedin.com. All fields typed and schema-versioned.
"profile_id": "in/johndoe", "full_name": "John Doe", "headline": "Senior Engineer at TechCorp", "location": "Bengaluru, Karnataka, India", "current_company": "TechCorp", "follower_count": 4218, "connection_count": "500+"
| # | profile_id | full_name | headline | location | current_company | current_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Pages objects from linkedin.com. All fields typed and schema-versioned.
"company_id": "techcorp-inc", "name": "TechCorp Inc.", "industry": "Software Development", "company_size": "1001-5000 employees", "follower_count": 85420, "employee_count_on_linkedin": 3412, "headquarters": "San Francisco, CA", "founded_year": 2012
| # | company_id | name | industry | company_size | follower_count | employee_count_on_linkedin |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Job Postings objects from linkedin.com. All fields typed and schema-versioned.
"job_id": "3849102938", "title": "Lead Backend Engineer", "company_name": "TechCorp Inc.", "location": "London, UK", "workplace_type": "Hybrid", "employment_type": "Full-time", "applicant_count": 47, "posted_date": "2026-05-10T14:30:00Z"
| # | job_id | title | company_name | company_id | location | workplace_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Education & Alumni objects from linkedin.com. All fields typed and schema-versioned.
"university_id": "stanford-university", "name": "Stanford University", "total_alumni": 342190, "alumni_by_company": "Google: 4500, Apple: 3200", "alumni_by_location": "San Francisco Bay Area: 85000", "alumni_by_function": "Engineering: 42000", "follower_count": 1205000
| # | university_id | name | location | total_alumni | alumni_by_location | alumni_by_company |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from linkedin.com. All fields typed and schema-versioned.
"keyword": "Data Engineer", "search_type": "PEOPLE", "position": 1, "entity_id": "in/janedoe", "entity_name": "Jane Doe", "primary_subtitle": "Data Engineer at DataFlirt", "location": "Bengaluru", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | search_type | position | entity_id | entity_name | primary_subtitle |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our LinkedIn scraper handles every layer of the platform: public profiles, company metrics, job postings, and alumni distributions - with JavaScript rendering and anti-bot circumvention built in.
Extract work history, education, skills, and certifications from public profiles without triggering authentication walls.
Track headcount growth, follower metrics, and employee distributions across departments and geographies.
Scrape active job postings, applicant counts, seniority levels, and required skills to map hiring trends.
Extract aggregated alumni data from university pages to track talent migration and hiring patterns.
Monitor week-over-week changes in company employee counts to signal growth or contraction.
Capture listed skills and endorsement counts to build talent density maps for specific regions.
Link employee profiles to company pages and university pages via structured identifiers.
Localise extraction using region-specific proxies to bypass geographic content restrictions.
Run one-off bulk exports or configure continuous pipelines at weekly or daily cadences.
Brief in. Clean data out.
Provide company lists, job search URLs, or profile directories. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for linkedin.com.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
LinkedIn invests heavily in scraping detection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
LinkedIn employs aggressive rate limiting and bot detection via TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing.
LinkedIn forces login for deep profile views. We utilise public directory structures, sitemaps, and search engine caches to extract public profile data without requiring authenticated sessions.
Company pages and job search results rely heavily on client-side rendering. We run full Playwright browser sessions to hydrate dynamic content and lazy-loaded lists.
LinkedIn frequently alters its DOM structure and obfuscates CSS classes. We rely on structured JSON-LD data and multi-layer fallback chains to maintain pipeline stability.
For large company tracking, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Recruitment firms map talent pools, track candidate movement, and identify passive candidates based on skill criteria.
Sales teams enrich CRM records with current titles, company affiliations, and headcount data to score leads.
PE and VC firms track headcount growth, executive turnover, and hiring velocity as leading indicators of company health.
Economists and researchers analyse job postings and skill requirements to map macro employment trends.
Corporate strategy teams monitor competitor hiring patterns to infer product roadmaps and geographic expansion.
Universities track graduate career trajectories, top employers, and geographic distribution for institutional reporting.
"LinkedIn holds the world's professional graph, but querying it at scale requires navigating aggressive rate limits and complex authentication walls."
Extracting professional data at volume requires sophisticated residential proxy networks, public directory traversal, and constant schema maintenance. DataFlirt manages the extraction infrastructure so your data science teams can focus on talent mapping and market analysis.
Everything supported by our linkedin.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles orchestration and retry logic. Playwright handles JavaScript rendering and interaction flows for complex search interfaces.
Residential ISP proxies rotate per request. We spoof TLS fingerprints and manage session state to avoid detection and rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About linkedin.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible, reinforced by the hiQ Labs v. LinkedIn ruling. DataFlirt extracts only public, non-authenticated profile and company data. We do not bypass authentication walls to access private data.
We do not use authenticated accounts. We rely on public directory structures, sitemaps, and search engine caches to access public-facing profiles and company pages, ensuring compliance with public data extraction principles.
No. We only extract data that users have explicitly made public. We do not extract private emails, phone numbers, or profiles hidden behind network privacy settings.
Job pipelines can be configured to run daily or hourly, capturing new postings, applicant count updates, and delistings in near real-time.
Yes. We can monitor company pages on a weekly or daily cadence to track changes in listed employee counts, followers, and departmental distributions.
Our packages start at defined lists (e.g., 5,000 companies or 50,000 profiles) with regular delivery. Contact us for a scoped quote based on your target volume.
We distribute requests across large pools of residential IPs, randomise request intervals, and employ strict concurrency limits to mimic natural browsing patterns and avoid IP bans.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of job postings or a weekly snapshot of competitor headcount - we scope, build, and operate the pipeline.
