LinkedIn Profile Dataset

Name: LinkedIn Profile Dataset
Creator: Kipplo
License: https://www.kipplo.com/terms-conditions/

Live

Every row is one person at work, so you get their job title, seniority, department, employer and location, plus any work email and direct dial we have for them. Narrow 250M+ profiles down to the people you actually want to reach, and read the real rows before you spend anything. Then export ten of them, or ten thousand. You pay for rows, not for fields, so the email and the phone number on a row you have already bought cost you nothing extra.

Data can be accessed via

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Records

250M+

Refresh

Continuous

Coverage

Global

Compliance

GDPR & CCPA ready

Pricing

Pay per row

Preview

See the rows first

Filtering and previewing is free. Credits are only spent on the rows you export.

app.kipplo.com/explorer · linkedin_people_profiles

SeniorityDepartmentJob title250M+ rows

full_name	job_title	seniority	department	company_name	city	email_status
Adela B.	Co Founder	C-Team	General Business & Management	Density Labs	Guadalajara	Valid
Alejandro C.	Regional Vice President	VP	General Business & Management	Fastenal	Tijuana	Valid
Adriana V.	Head of Cx, B2c	Director	Other	Logitech	Guadalajara	Valid
Aaron G.	Manufacturing Engineering Manager	Manager	Engineering	Aptiv	Reynosa	Valid
Aashi S.	Application Development Senior Analyst	Staff	Information Technology	Accenture México	Monterrey	Valid

Schema

All 74 fields in the LinkedIn Profile Dataset

Every row carries 74 fields, organised into the 10 groups below. Each field is listed with its type and what it holds. Filtering and previewing all 74 in Data Explorer costs nothing.

Field	Type	Description
Person · 14 fields
_id	integer	Kipplo record identifier
full_name	string	Full name as published on the profile
firstname	string	Given name
middlename	string	Middle name where published
lastname	string	Family name
linkedin_url	string	Canonical profile URL. The stable key for matching
linkedin_headline	string	Profile headline text. e.g. Sr NPI Engineer en Masimo
job_title	string	Job title as published on the profile. e.g. Ingeniero Sénior
job_description	string	Role description text where published
seniority	enum	Normalised seniority tier. e.g. Staff
department	enum	Normalised function. e.g. Engineering
about	string	Profile summary text
Skills	string	Skills listed on the profile. e.g. Apex Programming, C (Programming Language), C+…
Specialities	string	Specialities listed on the profile. e.g. noninvasive patient monitoring technologies
Contact · 4 fields
business_email	string	Business email address
email_status	enum	Deliverability status of the business email. e.g. valid
secondary_email	string	Additional email where one is known
cell_phone	string	Direct dial where a validated number exists
Person location · 5 fields
country	enum	Country the person is based in. e.g. Mexico
state	string	State or region. e.g. Baja California
city	string	City. e.g. Mexicali
country_region	enum	Macro region. e.g. NORAM
continent	enum	Continent. e.g. North America
Company · 7 fields
company_name	string	Employer name. e.g. Masimo
company_domain	string	Employer web domain. e.g. masimo.com
company_linkedin	string	Employer LinkedIn page. e.g. linkedin.com/company/masimo-corporation
company_description	string	Employer description text. e.g. Masimo (NASDAQ: MASI) is a global medical tech…
company_legal_type	string	Registered legal form. e.g. Limited
company_entity_type	enum	Public, private or other. e.g. Public Company
company_name_language	enum	Language of the company name. e.g. en
Company location · 10 fields
company_country	string	Country of the office. e.g. United States
company_state	string	State or region of the office. e.g. California
company_city	string	City of the office. e.g. Irvine
company_address	string	Street address
postcode	string	Postal code. e.g. 92618
company_phone	string	Company switchboard number. e.g. +1 949-929-7700
company_hq_country	enum	Headquarters country. e.g. United States
company_hq_state	string	Headquarters state. e.g. California
company_hq_city	string	Headquarters city. e.g. Irvine
location_count	integer	Number of offices. e.g. 1.0
Industry classification · 5 fields
linkedin_industry	string	Industry as published on LinkedIn. e.g. Medical Equipment Manufacturing
sic_codes	string	SIC classification codes. e.g. 3845
sic_description	string	SIC description. e.g. Mfg electromedical equipment
naics_codes	string	NAICS classification codes. e.g. 334510, 423450, 339112
naics_description	string	NAICS description. e.g. Electromedical and Electrotherapeutic Apparatu…
Size and scale · 5 fields
headcount_range	enum	Employee count band. e.g. 1001 to 5000
revenue_range	enum	Revenue band. e.g. $1B+
year_founded	integer	Year founded. e.g. 1989.0
employee_on_linkedin	integer	Employees found on LinkedIn. e.g. 3672.0
employee_on_linkedin_growth_rate	integer	Change in that count. e.g. 0.0
Technology and web · 6 fields
technologies	string	Technologies detected in use. e.g. AWS, Google, Google Cloud, Klaviyo, Microsoft …
Company Technologies	string	Technology list, alternate source
monthly_google_adspend	float	Estimated monthly Google ad spend. e.g. 12.24
total_monthly_traffic	float	Estimated monthly site visits. e.g. 14610.0
monthly_organic_traffic	float	Estimated monthly organic visits. e.g. 14610.0
monthly_paid_traffic	float	Estimated monthly paid visits. e.g. 0.0
Open roles · 12 fields
it_open_roles_count	integer	Open roles counted in it. e.g. 13.0
business_development_open_roles_count	integer	Open roles counted in business development. e.g. 1.0
customer_success_open_roles_count	integer	Open roles counted in customer success. e.g. 1.0
demand_generation_open_roles_count	integer	Open roles counted in demand generation. e.g. 0.0
devops_open_roles_count	integer	Open roles counted in devops. e.g. 0.0
grc_open_roles_count	integer	Open roles counted in grc. e.g. 0.0
marketing_open_roles_count	integer	Open roles counted in marketing. e.g. 0.0
account_executive_open_roles_count	integer	Open roles counted in account executive. e.g. 13.0
network_infrastructure_open_roles_count	integer	Open roles counted in network infrastructure. e.g. 7.0
operations_open_roles_count	integer	Open roles counted in operations. e.g. 3.0
sales_open_roles_count	integer	Open roles counted in sales. e.g. 44.0
security_open_roles_count	integer	Open roles counted in security. e.g. 0.0
Funding · 6 fields
lead_investors	string	Named lead investors. e.g. Politan Capital Management
total_funding_amount	integer	Total funding raised. e.g. 2500000.0
last_funding_amount	integer	Most recent round amount. e.g. 2500000.0
last_funding_date	string	Most recent round date. e.g. 2022-08-15
funding_round_num_investors	integer	Investors in the most recent round. e.g. 4.0
last_funding_type	enum	Most recent round type. e.g. Post-IPO Equity

All 74 columns in the dataset, grouped.

Download full data dictionary (CSV)

linkedin_people_profiles · single row

{
  "id":                                        "••••••",
  "full_name":                                 "A•••• G•••••",
  "firstname":                                 "A••••",
  "lastname":                                  "G•••••",
  "linkedin_url":                              "linkedin.com/in/••••••",
  "job_title":                                 "Manufacturing Engineering Manager",
  "seniority":                                 "Manager",
  "department":                                "Engineering",
  "about":                                     "••• redacted in this sample •••",
  "business_email":                            "••••@aptiv.com",
  "email_status":                              "valid",
  "secondary_email":                           "",
  "cell_phone":                                "•••••••9327",
  "country":                                   "Mexico",
  "state":                                     "Tamaulipas",
  "city":                                      "Reynosa",
  "company_name":                              "Aptiv",
  "company_domain":                            "aptiv.com",
  "company_linkedin":                          "linkedin.com/company/aptiv",
  "company_description":                       "Aptiv is a global technology company that designs, develop…",
  "linkedin_industry":                         "Software Development",
  "headcount_range":                           "10001+",
  "revenue_range":                             "$1B+",
  "year_founded":                              null,
  "company_country":                           "Ireland",
  "company_state":                             null,
  "company_city":                              "Dublin",
  "company_address":                           null,
  "company_phone":                             "+1 877-403-3544",
  "technologies":                              "AWS, Demandbase, Google, Google Cloud, IBM Cloud, Marketo,…",
  "sic_codes":                                 3714,
  "naics_codes":                               "541511, 336390, 541512",
  "middlename":                                "",
  "job_description":                           "••• redacted in this sample •••",
  "linkedin_headline":                         "Manufacturing Engineering Manager en Aptiv",
  "Skills":                                    null,
  "Specialities":                              "active safety, autonomous vehicles, connected cars, connec…",
  "employee_on_linkedin":                      33145,
  "company_legal_type":                        null,
  "postcode":                                  null,
  "company_name_language":                     "no",
  "company_entity_type":                       "Public Company",
  "employee_on_linkedin_growth_rate":          1,
  "monthly_google_adspend":                    0,
  "Company Technologies":                      null,
  "it_open_roles_count":                       317,
  "location_count":                            15,
  "business_development_open_roles_count":     1,
  "customer_success_open_roles_count":         1,
  "demand_generation_open_roles_count":        6,
  "devops_open_roles_count":                   12,
  "grc_open_roles_count":                      0,
  "marketing_open_roles_count":                9,
  "account_executive_open_roles_count":        24,
  "network_infrastructure_open_roles_count":   57,
  "operations_open_roles_count":               3,
  "sales_open_roles_count":                    26,
  "security_open_roles_count":                 28,
  "naics_description":                         "Custom Computer Programming Services, Other Motor Vehicle …",
  "sic_description":                           "Mfg motor vehicle parts/accessories",
  "lead_investors":                            null,
  "total_funding_amount":                      null,
  "last_funding_amount":                       null,
  "last_funding_date":                         "2024-09-09",
  "funding_round_num_investors":               1,
  "last_funding_type":                         "Post-IPO Debt",
  "country_region":                            "EMEA",
  "company_hq_country":                        "Switzerland",
  "company_hq_state":                          null,
  "company_hq_city":                           "Schaffhausen",
  "total_monthly_traffic":                     17190,
  "monthly_organic_traffic":                   17190,
  "monthly_paid_traffic":                      0,
  "continent":                                 "North America"
}
// all 74 columns · name, email and phone masked in this sample

Filters

Narrow it before you buy

These are the filters Data Explorer actually exposes. Applying them and previewing the results costs nothing. The ones marked BULK accept a pasted list of values rather than one at a time.

People

Contact NameBULK
Job TitleBULK
Department
Seniority
Location
LinkedIn URLBULK
Data Available
Exclude

Company

Company NameBULK
Company Location
Industry
NAICS CodesBULK
SIC CodesBULK
Employee Headcount
Revenue
Founded Year
Technologies

Use cases

What people use this data for

Three jobs it does well.

Building an outbound list

Pick the seniority, the function and the countries you sell into, and the list is built. The work emails are already on the rows, so there is no enrichment step afterwards and no second tool to pay for.

Finding candidates

Search by title, seniority and department to find the people already doing the job. Your whole hiring team can search the same data without anyone buying a recruiter seat.

Sizing a market

Count how many buyers in a role and region actually exist before you build a plan around them. You are counting real rows, not scaling up from a sample.

Access

How your team gets the data

Data Explorer and the API are the two ways in, and both read the identical underlying tables. If your team would rather not log in at all, we can send your filtered dataset to your own cloud on request.

Data Explorer

Data Explorer is where your team actually works with the data. Open a dataset in the grid, see the real fields and what is in them, filter down to the rows you want, then export as CSV, Excel, JSON, XML or SQL.

API access

The Kipplo API is the same data without the interface. Query records with the same filters you would use in the grid, and pull the results straight into your own product or workflow.

Delivery to your cloudOn request

Delivery is the exception, not the default route. If your team would rather not log in at all, we send your filtered dataset to your preferred cloud storage, once or on a schedule, in whatever format and structure you already work with.

Export formatsCSVXLSXJSONXMLSQL

Data pipeline

How we ensure quality

Every record goes through a rigorous 5-step process before it reaches you.

1
Data Sourcing
We gather B2B data from 50+ verified public and licensed sources.
2
Cleansing & De-duplication
Data is standardized, cleaned, and deduplicated to eliminate inconsistencies.
3
AI Validation
Our Advanced models cross-check multiple data points to ensure accuracy.
4
Human Verification
Our data team reviews and enriches flagged records for additional reliability.
5
Continuous Monitoring
Data is continuously monitored and refreshed to stay current and accurate.

Before you buy

Each row is one professional: full name, LinkedIn URL, headline, job title, seniority, department, company name and location, plus any business email and direct dial we have for them. You export it from Data Explorer as a flat table in CSV, XLSX, JSON, XML or SQL.

You pay per row exported, not per field. The number of premium fields on a row, such as an email or a direct dial, does not change what that row costs. Filtering and previewing in Data Explorer is free.

Yes. Every profile row carries the person and their employer together, so company name, domain, industry, headcount, revenue, technologies, open roles and funding all sit on the same row.

Profiles are updated continuously. When someone changes job or gets promoted, we pick it up and update their record. Every update goes through the same checks as a new one.

Yes, on request. Book a demo and we will set you up with a free trial so you can check the data before you commit.

See the rows before you buy

Filter the profile dataset in Data Explorer and read the real rows first. Previewing costs nothing, and one subscription covers every Kipplo dataset. You only pay for the rows you export.

Book a demoor email sales@kipplo.com