LinkedIn Public Job Leads

This Actor searches publicly accessible LinkedIn job listings with Crawlee's CheerioCrawler and writes matching job details to the default dataset. It does not log in, use cookies, or attempt to bypass access controls or rate limits.

Quick Start

Once you've installed the dependencies, start the Actor:

$apify run

Once your Actor is ready, you can push it to the Apify Console:

apify login # first, you need to log in if you haven't already done so

apify push

Project Structure

.actor/
├── actor.json # Actor config: name, version, env vars, runtime settings
├── dataset_schema.json # Structure and representation of data produced by an Actor
├── input_schema.json # Input validation & Console form definition
└── output_schema.json # Specifies where an Actor stores its output
src/
└── main.js # Actor entry point and orchestrator
storage/ # Local storage (mirrors Cloud during development)
├── datasets/ # Output items (JSON objects)
├── key_value_stores/ # Files, config, INPUT
└── request_queues/ # Pending crawl requests
Dockerfile # Container image definition

For more information, see the Actor definition documentation.

Input

• keywords (string): search terms, for example Desenvolvedor Frontend.
• location (string): search location, for example Brasil.
• maxResults (number): maximum number of job detail pages to process.

The Actor generates LinkedIn public search pages in batches of 25 and follows discovered job links. Requests are limited to a concurrency of two.

Output

Each dataset item contains jobTitle, companyName, companyLinkedInUrl, publishedAt, jobUrl, and googleLeadFinderUrl. The Google URL searches LinkedIn for public company decision-maker profiles; it does not collect those profiles.

What's included

• Apify SDK - toolkit for building Actors
• Crawlee - web scraping and browser automation library
• Input schema - define and easily validate a schema for your Actor's input
• Dataset - store structured data where each object stored has the same attributes
• Cheerio - a fast, flexible & elegant library for parsing and manipulating HTML and XML
• Proxy configuration - rotate IP addresses to prevent blocking

Resources

• Quick Start guide for building your first Actor
• Video tutorial on building a scraper using CheerioCrawler
• Written tutorial on building a scraper using CheerioCrawler
• Web scraping with Cheerio in 2023
• How to scrape a dynamic page using Cheerio
• Integration with Zapier, Make, Google Drive and others
• Video guide on getting data using Apify API

Creating Actors with templates

Getting started

For complete information see this article. To run the Actor use the following command:

$apify run

Deploy to Apify

Connect Git repository to Apify

If you've created a Git repository for the project, you can easily connect to Apify:

1. Go to Actor creation page
2. Click on Link Git Repository button

Push project on your local machine to Apify

You can also deploy the project on your local machine to Apify without the need for the Git repository.

1. Log in to Apify. You will need to provide your Apify API Token to complete this action.

   $apify login
   

2. Deploy your Actor. This command will deploy and build the Actor on the Apify Platform. You can find your newly created Actor under Actors -> My Actors.

   $apify push
   

Documentation reference

To learn more about Apify and Actors, take a look at the following resources:

• Apify SDK for JavaScript documentation
• Apify SDK for Python documentation
• Apify Platform documentation
• Join our developer community on Discord