LinkedIn Job Scraper

A powerful scraper that collects fresh LinkedIn job listings and delivers structured, actionable employment data. It helps automate job research, salary intelligence, and recruitment workflows without needing an account or cookies.

This LinkedIn Job Scraper is ideal for analysts, recruiters, researchers, and developers who need accurate job information at scale.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This project extracts detailed job posting data from LinkedIn, including titles, locations, compensation, company information, posting dates, and more. It solves the challenge of gathering structured hiring data from LinkedIn, which is otherwise time-consuming to collect manually. It's built for recruitment teams, job market researchers, data scientists, and automation engineers.

Why Use This Scraper?

• No login or session cookies required.
• Supports keyword and location filters for targeted job collection.
• Can fetch postings from recent timeframes (hours, days, weeks, etc.).
• Allows scraping by explicit job IDs for precise data capture.
• Automatically gathers metadata such as applicants, salary, seniority, and coordinates.

Features

Feature	Description
Keyword Search	Scrape job ads using job titles or search terms.
Location Filtering	Restrict results to specific cities, regions, or countries.
Time-Frame Filtering	Collect postings added in the last hours, days, weeks, or months.
Job ID Scraping	Fetch exact job ads using LinkedIn job IDs, ignoring other settings.
No Login Required	Works without credentials or cookies.
Company & Salary Data	Extract company info, pay range, benefits, and employment type.
High-Detail Output	Includes coordinates, seniority, posting age, and application status.

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What Data This Scraper Extracts

Field Name	Field Description
id	Unique LinkedIn job ID.
url	Direct link to the job posting.
title	Full title of the job listing.
description	Rich HTML job description.
employmentType	Type of employment (full-time, part-time, etc.).
datePosted	ISO timestamp when the job was posted.
company	Object with name, URL, and logo.
industry	Industry category of the employer.
location	Country, city, region, and coordinates.
applicants	Number of reported applicants.
shortTitle	Simplified job title.
validThrough	Expiration date for applications.
function	Job function (e.g., product management).
seniorityLevel	Required seniority.
salary	Salary range text.
baseSalary	Parsed min/max salary with currency.
monthsExperianceRequirements	Required months of experience.
applyUrl	URL where users can apply.
isClosed	Whether the job is closed.
isAcceptingApplications	Whether applications are still open.

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Example Output

{
    "id": 4172324909,
    "url": "https://www.linkedin.com/jobs/view/product-owner-at-maxar-technologies-4172324909",
    "title": "Maxar Technologies hiring Product Owner in Reston, VA | LinkedIn",
    "description": "Please review the job details below...",
    "employmentType": "FULL_TIME",
    "datePosted": "2025-03-25T13:54:15.000Z",
    "company": {
        "name": "Maxar Technologies",
        "url": "https://www.linkedin.com/company/maxar-technologies-ltd",
        "logo": "https://media.licdn.com/..."
    },
    "industry": "Defense and Space Manufacturing",
    "location": {
        "country": "US",
        "city": "Reston",
        "region": "VA",
        "longitude": -77.3545,
        "latitude": 38.960186
    },
    "applicants": 26,
    "shortTitle": "Product Owner",
    "validThrough": "2025-05-02T19:29:49.000Z",
    "function": "Product Management and Marketing",
    "seniorityLevel": "Entry level",
    "salary": "$81,000.00/yr - $135,000.00/yr",
    "baseSalary": {
        "currency": "USD",
        "unit": "YEAR",
        "min": 81000,
        "max": 135000
    },
    "monthsExperianceRequirements": 12,
    "applyUrl": "https://maxar.wd1.myworkdayjobs.com/MAXAR/job/Reston-VA/Product-Owner_R22005",
    "isClosed": false,
    "isAcceptingApplications": true
}

---

Directory Structure Tree

LinkedIn Job Scraper/
├── src/
│   ├── runner.py
│   ├── extractors/
│   │   ├── linkedin_parser.py
│   │   └── utils_time.py
│   ├── outputs/
│   │   └── exporters.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── inputs.sample.json
│   └── sample-output.json
├── requirements.txt
└── README.md

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Use Cases

• Recruiters gather targeted job postings automatically so they can speed up candidate sourcing.
• Market analysts collect salary and job trend data to study hiring patterns and market shifts.
• Career platforms aggregate listings to enrich their job boards with structured, up-to-date content.
• Data scientists use job data for modeling workforce demand and skill trends.
• Automation engineers integrate job extraction into workflow pipelines for continuous monitoring.

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FAQs

Can I scrape only jobs posted within a specific time range? Yes, you can filter by hours, days, weeks, months, or years. Entering “1 day” or “12 hours” limits results accordingly.

Can I scrape specific job IDs? Yes. Provide an array of job IDs and the scraper will return only those postings. Other settings are ignored.

Can I upload job IDs from a file? Currently, IDs must be provided directly in input parameters.

Does the scraper require proxies? Proxy usage is already handled internally to ensure stable performance.

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Performance Benchmarks and Results

Primary Metric: Average extraction speed of 40–60 job posts per minute depending on filters and region density. Reliability Metric: Stable success rate above 97% for accessible job URLs. Efficiency Metric: Optimized request batching reduces bandwidth use and avoids redundant fetches. Quality Metric: Data completeness typically exceeds 95%, capturing salary, coordinates, company info, and metadata where available.

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