LinkedIn Job Posting Dataset
LiveEvery row is one job posting, so you can see who is hiring, for what, and where. Titles, full descriptions, locations and listing dates sit alongside the employer’s industry, size and head office. Narrow 10M+ postings down to the roles you actually want, and pay only for the rows you export.
Data can be accessed via
Field dictionary
Every field in the table
Types and descriptions for all 18 columns, grouped into 4 sections. Filtering and previewing them in Data Explorer costs nothing.
| Field | Type | Description |
|---|---|---|
| The role · 4 fields | ||
| job_title | string | Title exactly as the employer posted it |
| job_description | text | Full text of the posting body |
| job_location | string | Location of the role as posted |
| source_domain | string | Domain the listing was sourced from |
| Dates · 2 fields | ||
| listed_at | date | When the listing was most recently seen live |
| expire_at | date | When the listing is set to expire |
| Hiring company · 6 fields | ||
| company_name | string | Name of the hiring company |
| company_linkedin_url | string | Company LinkedIn profile URL. The join key to the Company Dataset |
| company_description | text | Company self-description from its profile |
| company_specialities | array | Self-declared focus areas |
| company_industries | array | Industry labels for the hiring company |
| company_head_count | enum | Headcount band of the hiring company, such as 51 to 200 |
| Company location · 6 fields | ||
| company_location | string | Primary listed location of the hiring company |
| company_hq_country | string | Country of the company headquarters |
| company_hq_state | string | State or region of the headquarters |
| company_hq_city | string | City of the headquarters |
| company_hq_street | string | Street address of the headquarters |
| postal_code | string | Postal or ZIP code of the headquarters |
All 18 columns in the dataset, grouped.
Download full data dictionary (CSV){
"job_title": "Senior Account Executive",
"job_description": "We are looking for a Senior Account Executive to…",
"job_location": "Seattle, Washington, United States",
"source_domain": "careers.northwind.com",
"listed_at": "2026-09-08",
"expire_at": "2026-10-08",
"company_name": "Northwind Software",
"company_linkedin_url": "linkedin.com/company/northwind",
"company_description": "Northwind builds warehouse tooling for…",
"company_specialities": ["Logistics","SaaS"],
"company_industries": ["Software Development"],
"company_head_count": "1001 to 5000",
"company_location": "Seattle, WA",
"company_hq_country": "United States",
"company_hq_state": "Washington",
"company_hq_city": "Seattle",
"company_hq_street": "2211 Elliott Ave",
"postal_code": "98121"
}
// all 18 fields shownSample data
See the rows before you buy them
Real columns, in the exact shape they export.
| job_title | company_name | company_industries | company_hq_country | company_head_count | listed_at |
|---|---|---|---|---|---|
| Senior Account Executive | Northwind Software | Software Development | United States | 1001 to 5000 | 2026-09-08 |
| RevOps Manager | Lumen Labs | Software Development | India | 51 to 200 | 2026-09-07 |
| Data Engineer | Acme Inc | Manufacturing | Italy | 501 to 1000 | 2026-09-05 |
| Technical Recruiter | Brightpath | Staffing & Recruiting | United Kingdom | 201 to 500 | 2026-09-03 |
| Product Manager | Vertex | Financial Services | United States | 11 to 50 | 2026-09-01 |
| VP Engineering | Harbour Retail | Retail | Australia | 1001 to 5000 | 2026-08-12 |
Sample rows are illustrative. Request a real sample file cut to your own filters before you commit.
Filters
Narrow it before you buy
Every filter available on the Jobs table. Combine as many as you like. Filtering and previewing cost nothing, and you only pay for the rows you export.
The role
- Job title
- Job description
- Source domain
Hiring company
- Name
- LinkedIn URL
- Industries
- Headcount
Company HQ
- Country
- State
- City
Use cases
What teams build with hiring data at scale
For teams analysing thousands or millions of job postings.
Identify hiring signals
Spot companies expanding their teams and the functions they're investing in. Filter postings by title, industry and company size to prioritise accounts showing relevant hiring activity.
Track demand by role and market
Count postings by title, industry and location to see where demand is growing or cooling. The listed and expiry dates come on every row, so you can measure hiring volume over time in your own analysis.
Map the talent market
Identify employers competing for the same talent, see where demand is concentrated, and track shifts across locations and functions. Combine active and historical listings to understand how the market is changing.
Build intelligence products
Bring the postings into your own warehouse or product. Each row joins onto company records, so you can build recruiting tools, market trackers or research products on top of them.
Feed your intent models
Job descriptions name the tools and platforms an employer is hiring for. Pull those mentions out of the full text and you have a real signal of what a company is adopting, per account.
How it works
From first call to working data in four steps
It starts with a short call. You see the row count and the price before you commit. From there, filter and export your dataset in Data Explorer, query it through the API, or have it sent to your own cloud.
Tell us your segment
Industry, geography, seniority, company size, whoever you are trying to reach.
We check our coverage
A specialist checks what Kipplo actually holds for that segment and tells you how many rows match, so you know the data is there before you go any further.
Get your quote
Row count and price, before you commit.
Open your dataset
You get the complete dataset in Data Explorer and through the API, and filter it down yourself. Need it inside your own stack instead? On request we send it to your preferred cloud.
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.
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
Postings are 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
A bulk dataset of job listings published on LinkedIn, both active and closed, covering employers worldwide. It is built for teams working with postings at scale, not one lookup at a time.
There are 18 fields in total. They cover the job title and its full description, where the role is based, the dates the listing ran, and the company doing the hiring including its industry, headcount and head office address.
We measure 95%+ field accuracy across 50+ verified sources. Every posting is cleaned and deduplicated, checked by our models, then reviewed by our data team before it ships.
Data is continuously monitored and refreshed so job postings and company information stay current and accurate.
Three ways. Data Explorer, the Kipplo API, or files delivered to your cloud on request. Exports come as CSV, Excel, JSON, XML or SQL.
You pay per row exported, not per field. The number of columns on a row, such as the full job description or the company head office address, does not change what that row costs.
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.
Talk to our data experts
Every dataset starts with a short call. Tell us what you are building and a data specialist comes back within one business day.