LinkedIn Profile Data Extraction Fields: Education, Work History, About, and Headline

The hardest part of LinkedIn profile data extraction is not collecting text. It is turning that text into usable fields.

A profile page may show a name, headline, current company, about section, education, work history, location, and profile URL. But if the output is just one long text blob, it is not ready for a CRM, ATS, spreadsheet, enrichment workflow, or AI agent.

Useful extraction needs structure.

That means separating the profile into predictable sections:

1. Profile summary fields
2. Experience and work history
3. Education

This guide explains which LinkedIn profile data fields to extract, how to structure education and work history, how to handle headline and about text, and how to prepare the output for cleaning, enrichment, and CRM import.

For the broader HTML extraction workflow, see LinkedIn profile HTML data extraction. For a tool-based approach to collecting the data in the first place, see LinkedIn data extraction: a faster way to collect structured lead data.

Responsible use note

Use LinkedIn and website extraction workflows responsibly.

Only capture data that your team is permitted to access and process. Do not use extraction to bypass platform restrictions, login controls, rate limits, privacy settings, or consent requirements. Your internal policies and local laws matter as much as the technical workflow.

A good extraction process should be controlled, reviewable, and appropriate for the use case.

The three structured sections to extract

For most workflows, a LinkedIn profile extraction should produce three structured sections.

This structure matches how a human reads a profile.

A person first checks who the profile belongs to, then where they currently work, then their work history, then their education.

Your data workflow should preserve that structure instead of flattening everything into a single column.

Profile summary fields

The profile summary section usually contains the fields most sales and recruiting teams need first.

Recommended fields:

Do not assume the headline is always the current job title.

A headline might say:

Helping B2B teams scale outbound | Former VP Sales | Advisor

That is useful context, but it is not the same thing as:

VP Sales

The current job title should usually come from the current experience block when available.

Headline extraction rules

The headline is valuable because it often contains role context, market focus, or positioning.

But it should be stored as a raw text field, not over-parsed.

Good output:

Risky output:

A headline can include slogans, past roles, advisory roles, emojis, separators, and multiple claims. Use it for context and personalisation, but do not rely on it as the only current-role source.

A safer extraction workflow:

1. Capture headline as raw text.
2. Capture current job title from experience.
3. Capture current company from experience or top-card company link.
4. Use headline only as a fallback or supporting signal.
5. Flag conflicts for review.

About section extraction rules

The about section can be useful for research and personalisation, but it is not always clean.

It may include:

• Biography
• Sales copy
• Career summary
• Contact details
• Personal interests
• Keywords
• Achievements
• Line breaks
• Emoji
• Old company references

Recommended fields:

Do not stuff the entire about section into a CRM notes field without review. Long text can create messy imports, privacy concerns, and noisy AI personalisation.

For CRM workflows, the about section is best treated as research context, not a required structured field.

Experience and work history fields

The experience section is where most extraction mistakes happen.

A clean work history record should separate each role.

Recommended role fields:

The most important role is usually the current role, but previous roles can be useful for qualification, relationship mapping, and recruiting context.

How to identify the current company

The current company should not be guessed from the first company-like text on the page.

Use signals such as:

• A role with Present in the date range
• The top-card company link
• The first experience block
• Company page URL patterns
• Repeated company name near current title
• Role order in the experience section

When signals conflict, flag the record for review instead of silently choosing a value.

Example conflict:

In this case, Example Ventures is likely current company. Acme is historical context.

Handling grouped roles at one company

LinkedIn experience sections often group multiple roles under one company.

Example:

ExampleCo
3 yrs 6 mos

VP Sales
Jan 2024 - Present

Head of Sales
Jul 2022 - Dec 2023

Bad extraction flattens this into:

ExampleCo VP Sales Head of Sales Jan 2024 Present Jul 2022 Dec 2023

Good extraction creates separate role rows:

This matters because the CRM usually needs current title and company, but recruiting or research workflows may also need previous roles.

Education extraction fields

Education is useful for recruiting, alumni targeting, founder research, and relationship mapping.

Recommended education fields:

Education should be stored separately from work history.

Do not combine education into the same field as current company or role. It creates noisy records and makes downstream filtering harder.

Example JSON output

A clean extraction can look like this:

{
	"profile": {
		"full_name": "Jane Smith",
		"headline": "VP Sales at ExampleCo | B2B SaaS growth",
		"about_raw": "Sales leader focused on European B2B growth.",
		"location": "London, England, United Kingdom",
		"linkedin_url": "https://www.linkedin.com/in/janesmith/",
		"current_job_title": "VP Sales",
		"current_company_name": "ExampleCo",
		"current_company_url": "https://www.linkedin.com/company/exampleco/",
		"source": "LinkedIn",
		"captured_at": "2026-06-20"
	},
	"experience": [
		{
			"role_title": "VP Sales",
			"company_name": "ExampleCo",
			"company_linkedin_url": "https://www.linkedin.com/company/exampleco/",
			"start_date": "Jan 2024",
			"end_date": "Present",
			"is_current": true
		}
	],
	"education": [
		{
			"school_name": "University of Manchester",
			"degree": "BSc",
			"field_of_study": "Computer Science",
			"start_year": "2015",
			"end_year": "2018"
		}
	]
}

The exact schema can change by workflow, but the principle stays the same: separate profile, experience, and education.

Example CSV output

If the destination is a CRM, a flattened CSV may be easier.

full_name,headline,current_job_title,current_company_name,location,school_name,linkedin_url,source,captured_at
Jane Smith,VP Sales at ExampleCo,VP Sales,ExampleCo,London,University of Manchester,https://www.linkedin.com/in/janesmith/,LinkedIn,2026-06-20

For ATS or research workflows, keep a second table for experience history and another for education. A single CRM contact row cannot always represent every previous role cleanly.

A practical extraction prompt structure

If your workflow uses an assistant to parse HTML that your team is allowed to process, give it a strict schema.

A useful instruction pattern is:

You are a precise LinkedIn data extraction assistant.

You will receive two HTML sections:
1. Profile HTML
2. Experience section HTML

Extract three structured sections:
1. Profile summary
2. Experience / work history
3. Education

Return valid JSON only.
Do not guess missing fields.
Use null when a field is not present.
Mark current roles with is_current=true only when the date range or page context supports it.
Preserve the raw headline and about text separately from parsed job title and company.

The most important rules are “do not guess” and “separate raw fields from parsed fields.”

That keeps the output safer for cleaning and review.

For a lower-token approach to website extraction, see how to reduce AI token usage when extracting data from websites.

How Fetchr and DataFixr fit

Fetchr helps capture visible structured data from LinkedIn-style pages and websites through a browser-based workflow.

DataFixr helps with what happens next:

1. Clean names, companies, domains, and URLs.
2. Deduplicate by LinkedIn URL, email, phone, name, and company.
3. Enrich missing company or contact fields.
4. Validate emails, phones, websites, and LinkedIn URLs.
5. Review uncertain current-company matches.
6. Export CRM-ready records.

That handoff matters.

Extraction creates raw structured data. DataFixr turns that data into records that are safer to import, enrich, and activate. See the B2B data enrichment workflow, or get started to install Fetchr.

What teams say

“Being able to combine enrichment, deduplication, validation, and export prep in one workspace saves a lot of RevOps time.”

DataFixr customer

Final thought

LinkedIn profile data extraction works best when you resist the urge to capture everything as text.

Separate the profile summary, work history, and education. Preserve raw headline and about text. Parse current title and company carefully. Keep source URL and captured date. Review uncertain records before import.

That is how profile extraction becomes a usable sales, recruiting, or RevOps workflow instead of another messy spreadsheet.

Fetchr helps teams capture LinkedIn and website data from the browser. DataFixr helps clean, deduplicate, enrich, validate, and prepare those records for CRM import. Sign up to DataFixr to access Fetchr ->