Submit up to 1,000 LinkedIn profile URLs or handles and get each public profile back as structured data. This is personal data about identifiable people, so the rules that govern it are on this page rather than buried in a policy - read them before you sign up.
There's a line running through every use of profile data, and it isn't where most people assume. Finding someone to approach is one thing. Using what you found to decide something about them is another, and it's regulated.
Building a list of people who might be interested in a role, then approaching them professionally. This is what most of the recruitment industry does, and profiles are published partly to enable it.
Understanding who does what at a company you're already selling into, so an approach lands with the right person rather than the switchboard.
Do not use this to vet an applicant or decide an offer. In the US that makes it a consumer report requiring an FCRA-compliant provider - and profile data exposes age, ethnicity, gender and health signals, so deciding on it invites a discrimination claim.
Do not use it to track a named person over time, build a dossier, or contact someone in a personal rather than professional capacity. Accounts doing any of that get closed.
Worth saying plainly: sourcing a candidate and screening an applicant can involve the same data and are legally quite different. The first is finding someone to talk to. The second is making a decision that affects their livelihood, and it belongs with a provider licensed for it.
Two reasons, and the second one is the important one.
First, the reference doesn't publish them. It documents the request thoroughly - a profile URL or handle, up to 1,000 per run - and describes the response only as an array of results per query, without enumerating what a profile row contains.
Second, on a service returning personal data about named individuals, a guessed field list would mean advertising specific details about real people that we can't confirm you'll receive - invention with a person on the other end of it. So the data dictionary below is measured instead: ten columns that appeared in every export we hold, with a further eleven that appeared in only some runs listed separately rather than promised.
What to do instead: run a couple of profiles you already know on the free tier and read the file. That tells you exactly what this returns - and, just as usefully, lets you decide whether you actually want to hold all of it before you pull a thousand rows.
The exports come back in two shapes. These ten appeared in every file; a further eleven appeared in some runs and not others, so they are listed separately rather than promised.
Two caveats worth reading before you build on this. Eleven further columns appeared in 7 of the 12 exports but not the rest - query, address_country, address_locality, alumni_of, awards, description, iamge (spelled that way in the file), job_title, languages, url and works_for - so code against them defensively or not at all. And a profile that cannot be read still produces a row: one of the twelve came back with the name "Sign Up" and a favicon in image, which is what a blocked fetch looks like rather than a real person. Check name and image before trusting a row.
A URL or a handle is the whole input. The first step isn't technical.
Both forms work - a full URL like linkedin.com/in/example or just the handle example.
Each is professional-context work with a clear business purpose - which is what a legitimate-interest argument actually rests on.
Sourcing is the mainstream use of profile data and the reason much of it is published. Build a longlist for a role you're genuinely hiring for, approach people professionally, and drop anyone who asks to be removed. What keeps this defensible is that it stops at an approach - the decision-making happens later, with proper process.
Enterprise deals involve a dozen people whose roles aren't obvious from outside. Mapping the relevant team at a company you're already engaged with means your approach reaches the person who owns the problem, rather than being forwarded three times or ignored.
Aggregate rather than individual analysis: how a job title is defined across a sector, which skills cluster together, how teams are shaped. The output here is a pattern, not a person - and aggregate research is the easiest use to justify under any regime.
If your goal is reaching business contacts rather than researching individuals specifically, another service does it with less personal data and less to justify.
Leads & Contacts Enrichment starts from a company domain and returns named business contacts with job titles and work email addresses, each tagged with the source it came from. You get who to contact and how to reach them, in a business capacity, with a fully documented schema - and without collecting a career history, education record or anything else a profile carries that you probably don't need.
Similarly, if the question is about an organisation rather than its people, LinkedIn Companies Scraper reads company pages only and contains no personal data at all - eleven documented fields including an exact headcount. Data minimisation isn't just a compliance principle; less data is genuinely less work to hold responsibly.
No subscription, no minimum. Your first 500 rows are on us - and on this service, spending a few of them checking the output before you scale is the sensible move.
Every new account, one-time. No credit card required. Use a handful to see exactly what a profile row contains, then decide whether you want to hold all of it before pulling a thousand.
Roughly $2 per 1,000 profiles, one row per profile - exact to budget. Worth noting the cost of holding the data responsibly usually exceeds the cost of collecting it.
Volume pricing, SLAs and dedicated workers. For this service we'll want to understand the use case and your lawful basis before onboarding - not a formality, given what the data is.
LIVESCRAPER10 at checkout.Both of these carry less personal data than a profile pull, and both are fully documented.
Named business contacts with titles and work emails, from a domain. The right tool for reaching people in a professional capacity, with a documented schema and no career history attached.
Company pages rather than member profiles - eleven documented fields including an exact employee count, follower count and specialities, with no personal data in the output at all.
The most contested data category in this catalogue. Here is the honest position rather than a comfortable one.
A public profile is visible to anyone, and collecting publicly available information for research is long-established practice. That settles far less than people assume, because "publicly accessible" and "free to process" are different questions. A profile describes an identifiable person, so it is personal data, and under GDPR and comparable regimes you need a lawful basis to hold it at all - not merely to act on it.
The obligations that follow are yours as the controller, and two catch people out. Because you didn't collect the data from the person directly, you generally have to tell them you hold it, including where it came from. And they can object to your processing or ask for erasure, which means you need somewhere for that request to land and a way to act on it. Regulators in several jurisdictions have taken action over profile scraping, so this is enforced rather than theoretical.
Separately, LinkedIn's terms restrict automated access and the platform enforces them more actively than most sites. That's a terms question rather than a settled right, and it applies regardless of your data-protection position.
We touch nothing behind a login and exports auto-delete after 30 days. What we can't do is give you a lawful basis - nobody can sell you that. If you can't state yours in a sentence, talk to us before running anything.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
For most B2B outreach, Leads & Contacts Enrichment does the job with less personal data and a documented schema. If you do need profile research, talk to us about the use case and your lawful basis first - we'd rather get it right than take the order.
The LinkedIn Profiles Scraper reads public member profiles in bulk, accepting up to 1,000 profile URLs or handles per run - a full address or just the handle both work. It exists for professional-context research: sourcing candidates for roles you are genuinely hiring for, mapping who does what at an account you are already engaged with, and aggregate analysis of how roles and skills are structured across a sector. Those three uses share a shape worth noticing. Each has a clear business purpose, each stops at understanding or an approach rather than a decision, and each is the kind of processing a legitimate-interest argument can actually support.
This page deliberately publishes no field list. The reference documents the request side thoroughly and describes the response only as an array of results per query, never enumerating what a profile row contains. Elsewhere in this catalogue an undocumented schema would simply be inconvenient, and we would say so and move on. Here the missing information concerns personal details about identifiable people, and printing a plausible guess would mean advertising specific facts about real individuals that we cannot confirm you will receive. So nothing is named, and the recommendation is concrete instead: spend a few of the one-time 500 free rows on profiles whose contents you already know, read the export, and decide whether you want to hold everything in it before pulling a thousand more.
The compliance position deserves more space than a disclaimer. A public profile is visible to anyone, and collecting publicly available information for research is long-established practice - but publicly accessible and free to process are separate questions. A profile describes an identifiable person, making it personal data, and under GDPR and comparable regimes you need a lawful basis to hold it at all rather than merely to act on it. Two consequent obligations catch people out. Because the data did not come from the person directly, you generally have to tell them you hold it and where it came from; and they can object or request erasure, which means your process needs somewhere for that to land. Regulators in several jurisdictions have acted on profile scraping, so these are enforced duties rather than theoretical ones. Separately, LinkedIn's terms restrict automated access and the platform enforces them more actively than most, which is a terms question in addition to the data-protection one.
There is also a line that matters more than any technical detail: sourcing and screening are not the same activity even when they use the same data. Finding someone to approach about a role is ordinary recruitment work. Using what you found to vet an applicant or decide an offer is a decision about someone's livelihood - in the US that makes the data a consumer report, which may only be supplied by an FCRA-compliant provider, and profile data additionally exposes age, ethnicity, gender and health signals, so deciding on it invites a discrimination claim in any jurisdiction. That use is prohibited here, as is monitoring named individuals or approaching people in a personal capacity. For most B2B outreach the honest recommendation is a different product entirely: Leads & Contacts Enrichment returns named business contacts with titles and work email addresses from a company domain, with a fully documented schema and none of the career history a profile carries, and LinkedIn Companies Scraper reads company pages containing no personal data at all. Less data is less to justify and less to hold responsibly. If you are unsure whether your use qualifies, talk to us before running anything.