50 Types of LinkedIn Data You Can Get (You'll Be Surprised)

LinkedIn contains a ridiculous amount of useful professional data. If you are trying to figure out what kinds of LinkedIn data actually exist, what can be public, and what teams usually try to extract for sales, recruiting, enrichment, or research, this is the practical list.

That is the right framing for this topic. The hard part is not making a list of fields. The hard part is getting reliable, current, usable data without creating operational or legal pain for yourself.

However, LinkedIn has made access to its data notoriously difficult. That is not an accident. It protects the platform, protects member privacy, and pushes developers toward approved channels.

No matter that, this post still attempts to list, as exhaustively as possible, 50 types of LinkedIn data you can get your hands on. I am keeping the original structure intact because it was useful. I am just cleaning up what needed cleaning up.

A quick caveat before we get into the list: not every field below is public on every profile, and not every field is equally accessible through every tool, dataset, or workflow. Some are directly visible on LinkedIn. Some are inferred or enriched elsewhere. Some are much more trouble than they are worth.

All 50 Types of LinkedIn Data

Personal Data

1. First and last name: This sits at the top of a LinkedIn profile and is the most basic identity field.
2. Personal email: Used for account login and communication with LinkedIn. This is generally not public on the profile.
3. Work email: If shared elsewhere on the public web or resolved via enrichment, this is one of the most commercially useful fields for outbound.
4. Personal phone number: Sometimes discoverable through separate contact enrichment workflows, but not generally exposed publicly on LinkedIn.
5. Work experience history: Past and current roles, employers, tenure, and often short job descriptions.
6. Education history: Schools attended, majors, degrees, and dates where listed.
7. Profile picture: The public-facing headshot or avatar associated with the profile.
8. Current occupation: Current title and employer, usually the fastest way to place a person professionally.
9. Location (country, state, city): Usually exposed at a broad location level, sometimes more specific.
10. Languages: Languages spoken, where a user has chosen to list them.
11. Accomplishments: Awards, publications, projects, patents, test scores, and other structured bragging rights.
12. Licenses and certifications: Professional certifications, issuing organizations, and issue dates.
13. Skills: Skill tags that help summarize expertise, though the quality varies wildly because users self-report.
14. Volunteering experience: Causes, nonprofits, roles, and time spent volunteering.
15. Gender: Occasionally inferable elsewhere, but not a standard public LinkedIn field in the way people imagine.
16. Birth date: Not a standard public profile field and usually not available. I am retaining it here because it existed in the original article, but in practice you should treat this as rare or unavailable from LinkedIn itself.
17. Social media profiles: Links or handles for X, GitHub, personal websites, and other public identities.
18. Connections and followers: Signals for audience size and network breadth, though exact visibility depends on profile settings.
19. Posting activities: Posts, comments, reposts, and public activity that show what the person actually talks about.
20. Content interaction and engagement: Likes, comments, repost counts, and other engagement around visible content.
21. Interests and groups followed: Followed creators, companies, newsletters, groups, and adjacent professional interests.

Company Data

1. Company name: The official company name as it appears on LinkedIn.
2. Company profile picture: Usually the company logo or brand image.
3. Website: The company website listed on the company page.
4. Company size: A headcount band or employee count signal associated with the company.
5. Funding data: Funding rounds, investors, and total capital raised, where available through enrichment or third-party datasets.
6. Stock info: Public market identity for listed companies, if applicable.
7. Acquisitions: Acquired companies or acquisition history associated with the business.
8. HQ addresses: Headquarters location and sometimes additional office locations.
9. Industry type: The category or sector the company places itself in.
10. Specialties: Self-described areas of expertise, products, or services.
11. Year founded: The founding year shown on the company page.
12. Tagline: A short brand statement or company description line.
13. Employees: The people currently associated with the company and their titles.
14. Job titles and functions: The role distribution inside the company, which matters a lot for recruiting and sales segmentation.
15. Posts, content, activities, events: Company posts, employer-branding content, announcements, and public activity.
16. Similar companies: Adjacent businesses in the same category or market.
17. Categories: The labels, industry buckets, and discoverability context tied to the company page.

Jobs Data

1. Open jobs: Current vacancies associated with a company.
2. Job seekers: Members who indicate they are open to work or open to opportunities.
3. Job titles: Titles of open roles.
4. Job descriptions: Responsibilities, requirements, and hiring criteria for listed positions.
5. Location: Geography of the role, including remote, hybrid, or onsite where specified.
6. Industry: The industry context for the employer and the role.
7. Employment type: Full-time, part-time, contract, internship, temporary, and so on.
8. Job functions: Function tags or departmental alignment for the role.
9. Total applicants: Applicant count signals where LinkedIn shows them.

Groups Data

1. Groups on LinkedIn: Communities centered around industries, functions, interests, or shared professional goals.
2. Members: Member count inside a LinkedIn group.
3. Members interactions and engagements: Post activity, discussion engagement, and how alive or dead the group actually is.

What This List Actually Tells You

The useful part of this list is not the number 50. It is the shape of the data.

LinkedIn data breaks down into four buckets:

• Person profile data: who someone is, where they worked, where they studied
• Company data: what a business is, how big it is, where it operates
• Jobs data: what the company is hiring for right now
• Activity data: what the person or company is doing in public

If I were building go-to-market workflows, I would care most about these fields first:

1. Current title
2. Current employer
3. Work history
4. Company website
5. Company size
6. Funding data
7. Open jobs
8. Public posting activity
9. Work email
10. Similar companies

That is enough to drive a surprising amount of outbound, account research, recruiting, and market mapping.

That comment is more honest than most landing pages in this category.

A quick reality check

Not all 50 of these fields are equally available.

A few are straightforward. Name, title, company, work history, company website, job posts.

A few are conditional. Skills, certifications, activity, follower counts, interests.

A few are either rare, private, inferred somewhere else, or simply not dependable enough to build a serious workflow around. Personal email, personal phone number, birth date, gender, exact connections.

So yes, there are 50 types of LinkedIn data you can think about. No, that does not mean you can cleanly and consistently pull all 50 at production quality.

One way you could get this data, historically

If you have been around this blog for a while, you will remember Proxycurl. I built Proxycurl years ago because the official LinkedIn API was restrictive and most of the third-party market was a mess.

Important update: Proxycurl has been sunset. I am the founder behind Proxycurl, and I now work on NinjaPear. I am leaving the historical Proxycurl context here because it is part of the original article and still explains how people used to think about LinkedIn-shaped enrichment. But if you are evaluating tools today, you should be looking at NinjaPear instead.

Proxycurl used to make it easy to access and retrieve LinkedIn-shaped data with just a few lines of code. The appeal was obvious: fresh enrichment, fast API calls, and a pay-as-you-go model that was far easier to adopt than five-figure enterprise contracts.

That original value proposition was real. The market wanted:

• person profile enrichment
• company profile enrichment
• employee listing
• work email lookup
• jobs data

People still want those same primitives. They just want them now without the LinkedIn baggage.

What I would use now instead

This is where NinjaPear enters the picture.

NinjaPear does not scrape LinkedIn. That is the point. It gives you B2B data that is often richer, and in many cases more operationally useful, while avoiding the entire category of LinkedIn-derived legal exposure. If you care about that distinction, read this: none of the legal liability.

Instead of trying to replicate every last LinkedIn field 1:1, NinjaPear focuses on the fields that actually matter in production systems: company details, employee count, funding, updates, work email, person profile, customers, competitors, and business relationships.

Here is the closest practical mapping.

Use case	Historical Proxycurl shape	NinjaPear alternative	Data quality	Ease of use	Legal durability	Avg. score
Person enrichment	LinkedIn profile enrichment	Employee API / Person Profile endpoint	⭐⭐⭐⭐☆	⭐⭐⭐⭐☆	⭐⭐⭐⭐⭐	4.33/5
Company enrichment	LinkedIn company profile	Company API	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	5.00/5
Work email lookup	Email lookup	Work Email Lookup	⭐⭐⭐⭐☆	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	4.67/5
Employee count	Company size / headcount	Employee Count	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	5.00/5
Company activity	LinkedIn company posts	Company Updates	⭐⭐⭐⭐☆	⭐⭐⭐⭐☆	⭐⭐⭐⭐⭐	4.67/5
Competitive mapping	Similar companies	Competitor API	⭐⭐⭐⭐⭐	⭐⭐⭐⭐☆	⭐⭐⭐⭐⭐	4.67/5
Customer discovery	Not a native LinkedIn strength	Customer API	⭐⭐⭐⭐⭐	⭐⭐⭐⭐☆	⭐⭐⭐⭐⭐	4.67/5

A few concrete details matter here.

• NinjaPear's Person Profile endpoint costs 3 credits per call.
• Company Details costs 3 credits per call, or up to 6 with extra flags.
• Company Funding costs 2 credits per call + 1 credit per unique investor.
• Company Updates costs 2 credits per call.
• Work Email Lookup costs 2 credits when found, 0.5 credit on miss.
• You get a 3-day free trial with 10 credits included.
• Paid API endpoints are rate limited at 50 requests per minute.

That is not hand-wavy positioning. Those are the actual knobs that determine whether an API is usable inside a real workflow.

The better alternative to LinkedIn data

The original article framed the problem as: how do I get LinkedIn data?

I think that framing is now incomplete.

The better question is: what business data do I actually need, and can I get it in the same shape without tying my business to LinkedIn-derived risk?

In a lot of cases, the answer is yes.

For example:

• If you want company size, use NinjaPear's Employee Count endpoint.
• If you want company website, industry, executives, addresses, and founding year, use Company Details.
• If you want funding rounds and investors, use Company Funding.
• If you want recent company activity, use Company Updates across blogs and X.
• If you want work email + public professional profile, use Work Email Lookup plus Person Profile.
• If you want similar companies, use the Competitor API.
• If you want customer evidence, use the Customer API.

That last one matters a lot.

LinkedIn can tell you a company exists. It usually cannot tell you who buys from them with evidence. NinjaPear can. That is why I say the data is often richer, not just safer.

What I would actually prioritize

If you are building sales, recruiting, enrichment, or research systems, I would rank the data types in this article like this:

Tier 1

• Name
• Current title
• Current employer
• Work history
• Company website
• Company size
• Work email
• Open jobs

Tier 2

• Funding data
• Executives
• Locations
• Industry
• Company updates
• Similar companies
• Job descriptions

Tier 3

• Groups
• Followers
• Engagement counts
• Certifications
• Interests
• Volunteer work

Tier 4

• Birth date
• Gender
• Personal phone number
• Personal email

The lower the field sits on this list, the less I want my workflow to depend on it.

That is not a moral statement. It is an operational one.

That is the other practical reason to stop fetishizing LinkedIn as the source of truth for everything. Even when something is technically extractable, it may still be a bad foundation.

The updated bottom line

Yes, there are at least 50 types of LinkedIn data you can conceptually get.

No, you probably do not need all 50.

You need the 8 to 15 fields that move revenue, improve targeting, enrich records, or tell you when a company is changing.

The original version of this article pointed people toward Proxycurl because that was the right answer at the time. That answer is now historical. Proxycurl has been sunset.

The modern answer is NinjaPear: the same general shape of enrichment and B2B intelligence, but sourced from the public web instead of LinkedIn, and built to avoid the legal and business fragility that comes with LinkedIn-derived data.

If your workflow still starts with "how do I scrape LinkedIn," I think you are asking the older question. Start with the fields you need, then pick the source that will not bite you later.

If you want to try that approach, start with NinjaPear's free trial, test the Company API, the Person Profile endpoint in the docs, and the Customer or Competitor APIs from the main product directory. You will know pretty quickly whether you needed LinkedIn data, or just better business data.