LinkedIn Datasets in 2026: Best Sources for Profile & Company Data
LinkedIn datasets can be categorized into profile data and company data:
- LinkedIn company data: Includes basic company information, detailed employee profiles, active job postings, emerging hiring trends, and engagement metrics.
- LinkedIn profile data: Involves public profile information, employment history, educational backgrounds, professional certifications, connection networks, and user profile activity.
We explore the best LinkedIn data providers and how to access structured datasets legally.
LinkedIn dataset features: Profile, company & Job posting data coverage
Whether you want to buy LinkedIn data from a verified provider or need a specific LinkedIn company dataset for market research, understanding the different types of data available is crucial.
Common fields across all providers:
The following fields are present in all three providers and were removed from the tables above for clarity:
- LinkedIn company dataset fields: LinkedIn URL, Name, Website URL, Industry, Description, Employee count, Founded year, Logo URL, Headquarters (employee count), Company type, Funding info, Headline, Country code.
LinkedIn data provider pricing: Subscription & per-record costs
Best LinkedIn data providers: Where to buy LinkedIn data?
Bright Data provides comprehensive LinkedIn dataset solutions covering LinkedIn profile datasets, LinkedIn company datasets, and job listings. Their marketplace is one of the most reliable platforms to buy LinkedIn data in bulk.
With Bright Data’s “request a custom dataset” feature, users can order datasets tailored to their specific requirements. Pricing is based on compute costs (data extraction) and record costs (per record).
Pricing:
- Free samples are available.
- One-time purchase (Marketplace): Starting at $250 for 100K records.
- Custom datasets: Typically start from $250 for one-time deliveries.
- Subscription plans: With continuous updates, pricing starts around $1,200+.
Coresignal offers both current and historical data (up to 5 years back). Their products include multi-source company datasets, raw data (both real-time and historical), API integrations, and the Historical Headcount API, which provides employee count trends over time.
Their ‘Historical Headcount API’ is particularly useful for tracking employee count trends and company growth over time.
Their API-linked databases are refreshed regularly (near real-time updates for many datasets). Dataset pricing depends on factors like contract length, source coverage, and location.
Pricing:
- Monthly plans start at $49/month and include 250 API credits.
- Free trial: 14 days, with 200 API credits for testing.
- Custom quotes: Required for larger or more complex datasets.
People Data Labs (PDL) is a B2B data provider specializing in datasets on individuals and companies. Their offerings include APIs such as the Company Search API and the Company Enrichment API, enabling seamless access to and integration of data into applications.
The datasets include attributes such as year founded, industry, website domain, and LinkedIn URL. PDL also provides a free company dataset and limited-access APIs (100 credits) for users to explore their services at no cost.
Pricing:
- Free trial: Includes a free company dataset and 100 API credits.
- Pro Plan: Starting at $98/month, with ~350 enrichment credits.
- Per-record (credit-based) pricing:
- Person Enrichment / Search: ~$0.28 per record → drops to ~$0.20 at scale.
- Company Enrichment / Search: ~$0.10 per record → down to ~$0.05 at scale.
- Other APIs (e.g., IP Enrichment, Person Identify) have different per-credit rates.
- Enterprise plans: Custom pricing for high-volume usage.
What is a LinkedIn dataset?
LinkedIn dataset is a collection of structured data obtained from the LinkedIn platform. LinkedIn data refers to user-generated content on the LinkedIn platform, including user profiles, company pages, industry trends, and events.
What data is included in the LinkedIn dataset?
A LinkedIn dataset includes different data points related to users, companies, and job postings. Here are major data points included in a LinkedIn dataset:
- LinkedIn profile dataset: Work experience title, position, current company, education, connections, avatar, skills, and endorsements.
- LinkedIn company dataset: Company page, industry size, # of followers, website, location, and employee counts.
- LinkedIn job posting dataset: Job title, date posted, number of applicants, requirements, company, and location.
Key categories of LinkedIn datasets and databases
LinkedIn datasets can be categorized by source and collection method. The main types of LinkedIn datasets include:
1. Public LinkedIn Datasets (API & Scraping):
can be accessed through LinkedIn’s public APIs and web scrapers. LinkedIn APIs provide a more reliable way to access LinkedIn data. However, APIs have rate limits and restrictions that regulate the volume and frequency of data requests.
Web scraping allows users to collect specific data points tailored to their specific requirements. However, web scraping may violate LinkedIn’s terms of service and data privacy regulations (e.g., GDPR).
2. Official proprietary data:
Data is available through its premium products and services, such as Sales Navigator and LinkedIn Talent Insights. Proprietary datasets provide businesses and recruiters with exclusive access to certain data points that may not be available through public datasets.
3. Commercial 3rd party LinkedIn datasets:
Collections of data obtained from third-party sources and supplemented with LinkedIn information.
How to access LinkedIn datasets?
1. LinkedIn API:
LinkedIn provides several APIs that allow developers to access and obtain data from the platform. Some of LinkedIn’s APIs include:
- LinkedIn Company API: Provides access to publicly accessible LinkedIn members’ profile data, such as name, headline, profile picture, and location.
- LinkedIn Profile API: Allows users to obtain company data, including company description, industry, and employee count.
2. Web scraping:
Web scraping techniques can be used to access and extract publicly accessible LinkedIn data. This method for obtaining LinkedIn data is suitable for large-scale web scraping projects.
3. Third-party data providers:
LinkedIn data providers are companies or platforms that offer access to LinkedIn datasets via third-party data sets, APIs, or web scraping tools. Some companies specialize in providing tailored LinkedIn datasets for specific use cases or industries.
4. Data enrichment services:
Integrate LinkedIn data with other data sources to provide a more comprehensive individual customer or prospect profile. These services can help sales and marketing teams better target potential customers.
Since data enrichment services typically focus on enriching individual data points, they may be limited to providing industry-level information. If you require broader insights that combine LinkedIn data with other sources, third-party data providers offer more extensive, targeted options.
Applications of LinkedIn Datasets
Recruitment and talent sourcing
LinkedIn datasets can help recruiters identify talent and streamline their talent acquisition process. LinkedIn datasets provide substantial information on professionals, including their skills, work experience, and education.
This information can be used to conduct a targeted candidate search, tailor talent acquisition strategies, and identify areas for improvement in employer branding efforts.
Market research and competitor analysis
LinkedIn datasets might be useful for market research and competitive analysis. Organizations can use the professional data accessible on LinkedIn to:
- Reveal industry trends like emerging technologies, popular job titles, and in-demand skills
- Benchmark a company’s performance against its competitors
- Identify potential partners or acquisition targets based on factors such as company size and industry.
Lead generation
LinkedIn datasets enable sales professionals to target the right prospects and create tailored outreach strategies.
They can analyze a prospect’s professional background, including their connections and interests, enabling them to create personalized outreach messages. Sales professionals can also uncover upcoming events and conferences using LinkedIn data, expanding their networks and identifying new leads.
Best practices for using LinkedIn data ethically and responsibly
Ensuring ethical web scraping practices when collecting and using data is important to avoid legal and ethical concerns. For example, HiQ Labs, a data analytics company, scraped publicly available LinkedIn profile data for a professional skill analysis.
However, LinkedIn sued HiQ Labs in 2019, alleging that HiQ Labs violated the Computer Fraud and Abuse Act (CFAA) by accessing LinkedIn’s data without authorization. The Ninth Circuit determined that HiQ Labs’ actions did not violate the CFAA because the data was publicly accessible.
- Follow LinkedIn’s terms of service: LinkedIn outlines data use limitations, prohibited use cases, and access restrictions for LinkedIn profiles. Adhere to LinkedIn’s terms of service to ensure that you are complying with their guidelines and policies.
- Compliance with data protection laws: It is essential to adhere to regional and industry-specific regulations, such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) in the United States.
- Secure storage: You can employ access controls to limit who can access your stored data, or use secure storage infrastructure, such as firewalls and intrusion detection systems, to protect it from unauthorized access.
- Anonymize data: There are numerous techniques for anonymizing data, including data masking, pseudonymization, and generalization. Data anonymization removes or alters personally identifiable information (PII) to help organizations protect privacy and comply with data protection regulations.
Scraping LinkedIn data vs. utilizing LinkedIn datasets: which is better?
LinkedIn strictly forbids scraping in its terms of service, and doing so may result in account suspension or legal consequences. Using LinkedIn datasets is a safer choice if you prioritize legal compliance and require well-structured data.
Scraping data directly from LinkedIn may provide more up-to-date information. However, web scraping may require technical expertise to set up and maintain web scrapers, and it can be time-consuming and resource-intensive compared to pre-built datasets.
On the other hand, pre-built datasets save time and resources, and using a dataset from a reputable data provider may eliminate legal and ethical concerns. However, pre-built datasets may not be tailored to specific requirements. It is crucial to select a data provider that allows users to modify the data to fit their unique needs.
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