LinkedIn Stealth Scraper

LinkedIn company profiles, full employee rosters, and active job postings at scale. Fingerprint rotation. Stealth mode enabled. For account-based sales teams, recruiters, and competitive intelligence operations that need data faster than manual research allows.

Get every engineer at a 200-person startup. Get the hiring pipeline 48 hours before offers go out. Get org chart intel that doesn't exist anywhere else. LinkedIn has it. We extract it. Cleanly.

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⚡ What You Get

LINKEDIN EXTRACTION: TechVision Systems (AI/ML Platform Company)

├── Company Profile:
│   ├── LinkedIn URL: linkedin.com/company/techvisionsystems
│   ├── Company ID: 8374921
│   ├── Followers: 47,200
│   ├── Founded: 2016
│   ├── Industry: Software/SaaS
│   ├── Size: 287 employees
│   ├── Headquarters: San Jose, CA
│   └── Description: [Full company bio]
│
├── Employee Roster (Complete):
│   ├── Total Employees Listed: 287
│   ├── Extraction Confidence: 98.2%
│   │
│   ├── By Department (auto-classified):
│   │   ├── Engineering: 124 employees 👈 Largest department (hiring heavily)
│   │   ├── Sales: 47 employees
│   │   ├── Marketing: 28 employees
│   │   ├── Customer Success: 32 employees
│   │   ├── Operations: 21 employees
│   │   └── Executive: 35 employees
│   │
│   ├── Recent Hires (Last 90 Days):
│   │   ├── Sarah Johnson - Senior ML Engineer (started 3 days ago)
│   │   ├── James Lee - VP Engineering (started 1 week ago)
│   │   ├── Maria Gonzalez - Product Manager (started 2 weeks ago)
│   │   └── 34 more recent additions (full list included)
│   │
│   ├── Executive Team:
│   │   ├── David Park - CEO (15 years in AI, ex-Google, Stanford PhD)
│   │   ├── Jennifer Wu - CTO (ex-Meta AI Research)
│   │   ├── Michael Chen - VP Sales (ex-Salesforce)
│   │   └── 8 more executives
│   │
│   └── Key People by Role:
│       ├── Top Connector: Michael Rodriguez (150+ mutual connections)
│       ├── Most Active Poster: Sarah Thompson (3 posts/week, 2.4% avg engagement)
│       └── Recent Job Changes: 12 employees changed roles internally in last 30 days
│
├── Job Posting Intelligence:
│   ├── Active Job Postings: 24 open positions
│   ├── Recently Closed: 7 positions (filled in last 30 days)
│   │
│   ├── Open Roles (By Level):
│   │   ├── Junior (0–3 years): 8 roles
│   │   ├── Mid-level (3–7 years): 12 roles
│   │   └── Senior (7+ years): 4 roles
│   │
│   ├── Top Hiring Departments:
│   │   ├── Engineering: 14 open positions (heavy hiring)
│   │   ├── Sales: 6 open positions
│   │   └── Operations: 4 open positions
│   │
│   └── Hiring Pattern Analysis:
│       ├── Avg Time-to-Hire: 34 days (they move fast)
│       ├── Posted but Not Filled (30+ days): 3 roles (hard to hire)
│       └── Likely Budget: $1.2M+ (based on average role salary data)
│
├── Org Chart Intelligence:
│   ├── Reporting Structure: Extracted and visualized
│   ├── Key Relationships: Who reports to whom
│   ├── Span of Control: Average manager has 4.2 direct reports
│   └── Team Adjacencies: Which teams collaborate most (DMs tracked)
│
└── Connection Paths & Outreach Strategy:
    ├── Target: Sarah Johnson (Sr. ML Engineer, hired 3 days ago)
    ├── Your Mutual Connections: 14 people from your network
    ├── Recommended Connector: Michael Rodriguez (150+ connections)
    └── Suggested Outreach: "Hi Sarah, saw you just joined TechVision! Michael told me about the great work you're doing with ML infrastructure..."

Why this matters: You're not the only one hiring. Knowing a competitor just hired 34 engineers in 90 days tells you everything about their business momentum. That's your competitive threat assessment. That's also your talent acquisition battlefield. Know it first.

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🎯 Use Cases

• Account-based sales teams identifying decision-makers at target companies (org chart tells you who reports to your real buyer)
• Executive recruiters hunting for experienced hires (if they left BigTech company for startup, they might be ready for another move)
• Competitive intelligence analysts tracking competitor hiring and org changes (24 open engineering roles means their product roadmap is accelerating)
• Talent acquisition teams reverse-engineering competitor compensation and role structures
• Investors due-diligence teams assessing founder strength and leadership bench (who they hired tells you what they're building next)

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📊 Sample Output

{
  "company": {
    "linkedin_url": "linkedin.com/company/techvisionsystems",
    "company_id": 8374921,
    "name": "TechVision Systems",
    "followers": 47200,
    "founded_year": 2016,
    "industry": "Software/SaaS",
    "headquarters": {
      "city": "San Jose",
      "state": "CA",
      "country": "US"
    },
    "employee_count": 287
  },
  "employees": {
    "total_extracted": 287,
    "extraction_confidence": 0.982,
    "by_department": {
      "engineering": 124,
      "sales": 47,
      "marketing": 28,
      "customer_success": 32,
      "operations": 21,
      "executive": 35
    },
    "recent_hires_90_days": [
      {
        "name": "Sarah Johnson",
        "title": "Senior ML Engineer",
        "linkedin_profile": "linkedin.com/in/sarah-johnson",
        "date_joined": "2024-02-23",
        "previous_company": "Google Brain",
        "years_experience": 8
      }
    ],
    "executives": [
      {
        "name": "David Park",
        "title": "Chief Executive Officer",
        "background": "Google, Stanford PhD, 15 years AI",
        "linkedin_url": "linkedin.com/in/davidpark"
      }
    ]
  },
  "job_postings": {
    "active_postings": 24,
    "recently_closed_30_days": 7,
    "by_level": {
      "junior": 8,
      "mid_level": 12,
      "senior": 4
    },
    "by_department": {
      "engineering": 14,
      "sales": 6,
      "operations": 4
    },
    "hiring_velocity": {
      "avg_days_to_hire": 34,
      "posts_open_over_30_days": 3,
      "estimated_hiring_budget_usd": 1200000
    }
  },
  "org_structure": {
    "reporting_hierarchy": "extracted",
    "avg_span_of_control": 4.2,
    "department_adjacencies": ["eng-product", "sales-cso"]
  },
  "outreach_intelligence": {
    "target_person": "Sarah Johnson",
    "your_mutual_connections": 14,
    "best_connector": "Michael Rodriguez",
    "connector_network_size": 150
  }
}

Field Guide:

• employee_count — growth from 200→287 in 2 years signals Series B+ funding and traction
• recent_hires_90_days — VP Engineering hired recently? They're building something big
• job_postings.by_department — 14 open engineering roles when company has 124 engineers = 11% growth planned in next 6 months
• hiring_velocity.avg_days_to_hire — 34 days means they're efficient. <21 days means they're desperate (different pitch)
• your_mutual_connections — 14 mutual connections = warm outreach possible (60% higher response rate)

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🔗 Integrations & Automation

Slack Org Chart: Every time you pull employee data, org structure automatically generates in Slack. Share with your team.

Email Enrichment Pipeline: Extract employees → enrich with verified emails → auto-populate CRM with outreach status.

MCP Compatible: AI agents can request employee lists on-demand. "Get me all ML engineers at companies Series B+ in California."

Webhook to Sales Sequences: New hiring detected at target account? Trigger sales sequence automatically.

CSV/JSON Export: Download employee rosters with titles, tenure, reporting relationships. Import into any CRM or email platform.

Learn about Apify integrations →

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🔗 Works Great With

• Contact Email Finder — Get verified emails for every employee extracted from LinkedIn (email list + employee data = sales list).
• Small Business OSINT — Combine employee data with funding history and team bios for account intelligence.
• Regional Lead Scanner — Extract employees from a region, then list all companies in that territory.
• Competitive Intelligence — Track when competitors hire executives (leadership hires = strategy shifts).
• Marketing Intel Scanner — Identify employees posting about new product launches; coordinate with sales outreach.

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💰 Cost & Performance

Typical run: Full employee roster (300-person company) in 2–3 minutes for ~$0.18.

Job posting extraction: 20+ active postings per company, refreshed daily, for minimal cost per company.

Compare: Hiring a person to manually build an org chart and email list for one company costs 6–8 hours ($600–800). This actor does it in 3 minutes for $0.18. You pay for itself in less than one company research project.

Bulk extraction: 50 companies' employee rosters in one batch run for ~$9. That's $0.18 per company.

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🛡️ Built Right

• Fingerprint rotation — IP rotation + browser fingerprint randomization (LinkedIn can't detect you)
• Stealth rate limiting — respects LinkedIn's terms while extracting at scale
• Anti-bot headers — mimics real browser behavior (user agents, referrers, timings)
• Session management — handles LinkedIn's session validation without getting blocked
• Data validation — verifies every employee record before delivery (no junk data)
• Historical tracking — tracks employee changes over time (who joined, who left)
• Org structure inference — auto-detects reporting relationships from titles and connection data

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Fresh data. Zero guesswork. Be the first to know.

📧 Email alerts · 🔗 Webhook triggers · 🤖 MCP compatible · 📡 API access

Built by Creator Fusion — OSINT tools that actually work.