Extract data from a LinkedIn profile

Two real runs

Below are two things this page was built for, run through it on September 25, 2026 and shown exactly as they came back. The first is a profile saved with LinkedIn's own More → Save to PDF; the person is fictional, so you can download the file and try it yourself. The second is two real company pages, read from their public URLs.

A saved profile PDF → JSON

Every field on the PDF came back, including the certification and both languages. Two things are worth knowing: dates come back as LinkedIn writes them ("March 2023 - Present"), not as ISO dates, unless you ask for a format in the box above the extract button; and a job with no description returns null rather than an invented one.

• Sample profile PDF (fictional, 57 KB)
• The JSON it produced

A company page URL → JSON

{
  "name": "Stripe",
  "tagline": "Help increase the GDP of the internet.",
  "industry": "Technology, Information and Internet",
  "company_size": "5,001-10,000 employees",
  "headquarters": "South San Francisco, California",
  "followers": 1728565,
  "website": "https://stripe.com",
  "founded": 2010,
  "specialties": [],
  "about": "Stripe builds programmable financial services."
}

{
  "name": "Microsoft",
  "tagline": "Software Development",
  "industry": "Software Development",
  "company_size": "10,001+ employees",
  "headquarters": "Redmond, Washington",
  "followers": 29164161,
  "website": "https://news.microsoft.com/",
  "founded": null,
  "specialties": [
    "Business Software",
    "Developer Tools",
    "Home & Educational Software",
    "Tablets",
    "Search",
    "Advertising",
    "… 17 more"
  ],
  "about": "Every company has a mission."
}

Look at what went wrong as well as what went right. Microsoft's page has no tagline in the public preview, so the model filled tagline with the industry, and the founding year isn't in the preview either, so it came back null. Stripe's page carries both. The "about" field is the first sentence only because that's what the request asked for. Follower counts move by the minute; these are the numbers on the day of the run.

• Stripe company page JSON
• Microsoft company page JSON (full specialties list)

What each input returns

What comes back depends on what LinkedIn shows the source, not on the extractor. A signed-out visitor, which is what a URL fetch is, sees the public preview; the saved PDF carries the full profile.

We don't sign in to LinkedIn and don't get around the login wall. When a field isn't in the source, you get null, not a guess.

Company pages to Excel or JSON

1. Switch the extractor above to the URL tab and paste a company URL, for example linkedin.com/company/stripe.
2. In the box above the extract button, list the fields you want: name, industry, company_size, headquarters, followers, website, specialties.
3. Extract, then download JSON, or Excel/CSV if you're building a list. Each field becomes a column.
4. For more than a handful, send each URL to the REST API with the same field list; the schema stays identical row to row, so the results stack into one sheet.

If you need thousands of company pages a day, a dedicated scraper is the better tool. Apify's LinkedIn Company API actor, for instance, takes up to 1,000 URLs per run, needs no login, and is priced from $4.85 per 1,000 companies. It returns a fixed set of fields. This page is for the other case: a few dozen companies, your own field names, and the same tool for the PDFs and pasted text that sit next to them.

Full name and current employer

The public preview of a profile, the static HTML LinkedIn serves before login, carries the name, the headline, the location and the current employer. That's why a profile URL is enough when those are all you need: paste it, pick "Full name & basics", and you get full_name, headline, location, current_company and current_title. If the headline says "Product Manager at Acme" but the experience block says otherwise, the PDF is the source of truth, because the headline is free text the person wrote.

LinkedIn URL path extraction

Sometimes the job is only the URL: turning linkedin.com/in/taylor-sample-demo into a stable key before an import. LinkedIn URLs follow a small set of patterns, and a regular expression is the right tool, not an extractor:

const m = url.match(/linkedin\.com\/(in|company|school)\/([^/?#]+)/i);
// m[1] → "in", m[2] → "taylor-sample-demo"

const post = url.match(/linkedin\.com\/posts\/([^_/]+)_.*activity-(\d+)/i);
// post[1] → author, post[2] → activity id

When the URL is buried in a document (a CV, an exported spreadsheet, a JSON file from another tool), use the "URL path segment" option above: it returns profile_url, the public identifier and the profile type. To pull LinkedIn URLs out of a JSON file you already have, jq does it in one line: jq -r '.. | strings | select(test("linkedin\\.com/"))' file.json.

LinkedIn ads data to JSON

Ad performance isn't on a profile or a company page, so no URL will return it. LinkedIn's own route is Campaign Manager: select the accounts, set the time range, press Export, and pick a report type (campaign, ad, conversion or demographics performance), which downloads as CSV. To turn that CSV into JSON with your own field names, paste its contents into the Text tab above and describe the fields, e.g. "one object per ad: ad_name, impressions, clicks, spend, conversions".

Sources

• Apify: LinkedIn Company API, input limits, fields and price, checked September 25, 2026
• LinkedIn Help: Export reports in Campaign Manager, checked September 25, 2026
• Examples above: runs of this page on September 25, 2026; see how we test