Scrape LinkedIn Profiles Without Login at Scale (2026)

Published April 27, 2026 · 1544 words · For recruiters

Thirdwatch's LinkedIn Profile Scraper returns public LinkedIn profile data at $0.01 per profile — name, headline, current and past positions, education, location, follower count, and articles — without requiring a LinkedIn account, cookies, or session tokens. Built for recruiters researching candidates, sales teams enriching prospect records, founders building their own outbound, and researchers studying career trajectories at scale.

Why scrape LinkedIn profiles without login

LinkedIn is the largest professional-profile dataset in the world. According to LinkedIn's own end-of-2024 disclosures, the platform hosts more than 1 billion member profiles. For a recruiter agency screening candidates, a sales team enriching prospect records, or a researcher studying executive career trajectories, that profile dataset is the canonical source. The blocker for most teams: LinkedIn does not offer a public profile API, and logged-in scraping approaches risk account suspension and break with every UI change LinkedIn ships.

A guest-mode scraper sidesteps both problems. LinkedIn's public profile pages render core profile data to anonymous visitors — that's the page Thirdwatch's actor reads. The job-to-be-done is structured. A recruiter wants every senior engineer at a target company enriched from a list of profile URLs. A sales team wants prospect records auto-filled from LinkedIn after a Crunchbase search. A founder wants every YC-founder profile pulled for an investor-research project. A market researcher wants 5,000 product manager profiles across FAANG to study career trajectories. All of these reduce to the same call: pass profile URLs in, get structured rows out.

How does this compare to the alternatives?

Three options for getting LinkedIn profile data into a pipeline:

Apollo and Lusha bundle email/phone enrichment LinkedIn doesn't publicly expose, but they're priced for go-to-market teams with full sales motions. The LinkedIn Profile Scraper actor page gives you the public profile fields — names, titles, education, articles — at pay-per-result pricing for teams that only need the LinkedIn layer.

How to scrape LinkedIn profiles in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token. Every example below assumes the token is in APIFY_TOKEN:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull profiles from a list of URLs or usernames?

Pass any mix of usernames, paths, or full URLs. The actor normalises all three.

import os, requests, pandas as pd

ACTOR = "thirdwatch~linkedin-profile-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

PROFILES = [
    "satyanadella",
    "/in/sundarpichai",
    "https://www.linkedin.com/in/jeffweiner08/",
    "lisasu",
    "patgelsinger",
]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={"profileUrls": PROFILES, "maxProfiles": 100},
    timeout=600,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} profiles enriched")

Five profiles cost $0.05; a 100-profile batch costs $1.

Step 3: How do I extract current employment and education in clean rows?

Flatten the nested arrays for downstream BigQuery / pandas analysis.

flat = []
for _, row in df.iterrows():
    primary_title = (row.get("current_titles") or [None])[0]
    primary_company = (row.get("current_companies") or [{}])[0]
    primary_edu = (row.get("education") or [{}])[0]
    flat.append({
        "name": row.get("name"),
        "headline": row.get("headline"),
        "title": primary_title,
        "company": primary_company.get("name"),
        "company_start": primary_company.get("start_date"),
        "school": primary_edu.get("school"),
        "degree": primary_edu.get("degree"),
        "location": row.get("location"),
        "followers": row.get("followers_count"),
        "profile_url": row.get("profile_url"),
    })
flat_df = pd.DataFrame(flat)
print(flat_df.head())

The flat_df is the canonical "one row per person" view recruiter and sales pipelines actually consume.

Step 4: How do I push to a CRM and dedupe by profile_url?

LinkedIn profile_url is stable per member; dedupe on it before upserting to HubSpot, Salesforce, or your sourcing tool.

import requests as r

HUBSPOT_TOKEN = os.environ["HUBSPOT_TOKEN"]
flat_df = flat_df.drop_duplicates(subset=["profile_url"])

for _, row in flat_df.iterrows():
    r.post(
        "https://api.hubspot.com/crm/v3/objects/contacts",
        headers={"Authorization": f"Bearer {HUBSPOT_TOKEN}"},
        json={"properties": {
            "firstname": (row["name"] or "").split(" ")[0],
            "lastname": " ".join((row["name"] or "").split(" ")[1:]),
            "jobtitle": row["title"],
            "company": row["company"],
            "city": row["location"],
            "linkedin_url": row["profile_url"],
            "linkedin_followers": row["followers"],
            "linkedin_headline": row["headline"],
        }},
        timeout=10,
    )
print(f"{len(flat_df)} contacts upserted to HubSpot")

Schedule the actor on Apify's scheduler at daily cadence keyed off a "freshly added profiles" list and the loop maintains itself.

Sample output

A single record for one profile looks like this. Five rows of this shape weigh ~5 KB.

{
  "name": "Satya Nadella",
  "headline": "Chairman and CEO at Microsoft",
  "current_titles": ["Chairman and CEO"],
  "current_companies": [{"name": "Microsoft", "start_date": "2014"}],
  "past_positions": [
    {"name": "EVP, Cloud and Enterprise", "company": "Microsoft", "start_date": "2011", "end_date": "2014"}
  ],
  "education": [
    {"school": "University of Wisconsin-Milwaukee", "degree": "MS", "field": "Computer Science"}
  ],
  "about": "As Chairman and CEO of Microsoft...",
  "location": "Redmond, Washington",
  "followers_count": 10000000,
  "profile_url": "https://www.linkedin.com/in/satyanadella/"
}

current_companies and past_positions are arrays because LinkedIn supports multiple concurrent roles per person — common for executives, board members, and advisors. followers_count is a useful proxy for influence/reach in the network. articles (when populated) gives recent publication titles and dates, useful for sales teams trying to find a recent talking-point for outreach.

Common pitfalls

Three things go wrong in production LinkedIn-profile pipelines. Username normalisation drift — LinkedIn occasionally changes profile URL formats (custom URLs vs auto-generated /pub/-style URLs). The actor handles all three input formats, but your CRM keying should standardise to the resolved profile_url returned in the output, not the input you sent. Restricted-profile rows — about 5-10% of profiles return only name + headline + photo because the user has restricted public visibility; treat these as flagged-but-kept rather than dropping them. Job-history truncation — LinkedIn shows full position history for some profiles and only the latest 5-7 positions for others; for a complete career-history analysis, the actor returns whatever LinkedIn renders publicly, and any gaps should be expected rather than treated as a scraper failure.

Thirdwatch's actor uses an Apify residential-proxy rotation by default and conservative concurrency to stay well within polite-crawling norms. The pure-HTTP architecture means a 100-profile pull completes in 5-10 minutes wall-clock and costs $1 — small enough to run continuously as part of a sourcing pipeline. A fourth subtle issue worth flagging is that LinkedIn occasionally surfaces "Locked" profile pages where the headline and current title are visible but everything else (about, education, past_positions) is gated behind login; the actor returns whatever LinkedIn renders publicly, so locked rows show partial data and are best treated as quality-flagged rather than retried — retrying without a login won't unlock them. A fifth note: company-name normalisation is your responsibility downstream — LinkedIn shows "Microsoft" for some employees and "Microsoft Corporation" for others on the same company page; build a synonyms map if cross-employee company-grouping matters in your CRM.

Related use cases

• Build a candidate shortlist from LinkedIn profiles
• Enrich your CRM with LinkedIn profile data
• Find decision-makers by title and company
• The complete guide to scraping job boards
• All Thirdwatch use-case guides

Frequently asked questions

Do I need a LinkedIn account or cookies?

No. Thirdwatch's LinkedIn Profile Scraper accesses only publicly visible profile pages — the same URLs anyone can view as a guest without signing in. The actor never logs in, never injects cookies, and never touches private data. This sidesteps the account-suspension risk that breaks most logged-in scraping approaches.

How much does it cost to scrape LinkedIn profiles?

Thirdwatch charges $0.01 per profile on the FREE tier and drops to $0.005 at GOLD volume. A 1,000-profile candidate-research batch costs $10 at FREE pricing or $5 at GOLD — competitive with Apollo, Lusha, and other contact-data tools that charge similar rates per record while bundling in features you may not need.

What fields are returned per profile?

Up to 12 fields per profile: name, headline, current_titles, current_companies (with start dates), past_positions (with company, title, date range), education (school, degree, field), about, location, profile_photo_url, followers_count, profile_url, and articles (with title, date, like count). Skills, endorsements, recommendations, and certifications are not returned because they require login on LinkedIn.

Can I pass usernames instead of full URLs?

Yes. The actor accepts three input formats: bare username (satyanadella), profile path (/in/satyanadella), or full URL (https://www.linkedin.com/in/satyanadella/). Mixing formats in the same profileUrls array works fine. For watchlists curated from external sources (Crunchbase, Hunter, internal CRM), pass whatever format you have without preprocessing.

How does this compare to Apollo or Lusha for sales prospecting?

Apollo and Lusha enrich profiles with email and direct-dial phone — fields LinkedIn does not publicly expose. They charge $50-$200/month per seat plus credit-based usage. Thirdwatch's actor returns the public LinkedIn data only (no email/phone) at pure pay-per-result pricing — typically 2-5x cheaper for the LinkedIn-data layer of a prospecting workflow. For email and phone, pair this with a contact-enrichment service downstream.

What if a profile is private or restricted?

Profiles with restricted visibility return a subset of the standard fields — typically only name, headline, and profile photo. The actor returns whatever LinkedIn shows publicly. Surface restricted-profile rows as a quality flag in your downstream pipeline rather than dropping them; they're often legitimate prospects who simply restricted their public profile.

Run the LinkedIn Profile Scraper on Apify Store — pay-per-profile, free to try, no credit card to test.