How to Scrape LinkedIn Employees Without Login or Sales Navigator

LinkedIn employee data is one of the most valuable B2B datasets. It is also one of the most locked-down. Here is what works without a Sales Navigator subscription.

What LinkedIn exposes publicly

Every LinkedIn company page has a People tab showing current employees. Without logging in, you can see:

• Employee name
• Current job title
• Location (city/country)
• LinkedIn profile URL

This is the publicly visible data — no account required. Sales Navigator adds department, seniority level, and more granular filters, but the base data is accessible without it.

Why scraping LinkedIn is tricky

LinkedIn actively blocks automated access. The challenges:

• Rate limiting on IP address
• Cookie-based session detection
• JavaScript rendering required (the People page is not static HTML)
• Anti-bot fingerprinting

The LinkedIn Employees Scraper handles these with residential proxy rotation and browser fingerprint management.

Basic usage

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "companyUrl": "https://www.linkedin.com/company/stripe/",
    "maxResults": 200,
    "titleFilter": "engineer",   # optional: filter by title keyword
}

run = client.actor('themineworks/linkedin-employees').call(run_input=run_input)

for employee in client.dataset(run['defaultDatasetId']).iterate_items():
    print(f"{employee['name']} — {employee['title']} — {employee['location']}")
    print(f"  {employee['profile_url']}")

Building a B2B lead list

Combine employee data with an email finder for complete lead enrichment:

results = list(client.dataset(run['defaultDatasetId']).iterate_items())

# Filter to decision-maker titles
decision_makers = [e for e in results if any(kw in e['title'].lower() for kw in
    ['vp', 'director', 'head of', 'chief', 'cto', 'cmo', 'cfo', 'founder']
)]

print(f"Found {len(decision_makers)} decision makers at target company")
for dm in decision_makers:
    print(f"  {dm['name']} — {dm['title']}")

Org chart mapping

Scrape multiple departments to build a lightweight org chart:

departments = ['engineering', 'sales', 'marketing', 'product', 'finance']
org_map = {}

for dept in departments:
    run_input = {
        "companyUrl": "https://www.linkedin.com/company/your-target/",
        "titleFilter": dept,
        "maxResults": 50,
    }
    run = client.actor('themineworks/linkedin-employees').call(run_input=run_input)
    org_map[dept] = list(client.dataset(run['defaultDatasetId']).iterate_items())

for dept, employees in org_map.items():
    print(f"{dept}: {len(employees)} people")

Rate of change tracking

Run monthly and diff results to track hiring patterns:

import json

# Compare this month vs last month
with open('employees_prev.json') as f:
    prev = {e['profile_url'] for e in json.load(f)}

current = list(client.dataset(run['defaultDatasetId']).iterate_items())
current_urls = {e['profile_url'] for e in current}

new_hires = current_urls - prev
departures = prev - current_urls
print(f"New hires: {len(new_hires)}, Departures: {len(departures)}")

Pricing

$0.002 per profile returned. Zero charge on empty searches.