LinkedIn Data Scraper with Django

Project Description:

I am looking for an experienced Python/Django developer with web scraping expertise to build a Django-based web scraper that logs into LinkedIn, scrapes a list of profiles from a specified search results page, stores the data in an SQLite database, and sends connection requests to each profile with a personalized message.

Core Requirements:

1. Django Web Application:

.Create a new Django project.

.Set up SQLite as the database.

2. LinkedIn Authentication:

.Implement login functionality to sign in to LinkedIn using provided username and password credentials (use a secure and configurable method for storing credentials).

3. Scraping Logic:

.Navigate to the following LinkedIn search results page:
https://www.linkedin.com/search/results/people/?keywords=%22ceo%22%20or%20%22founder%22%20or%20%22owner%22&origin=SWITCH_SEARCH_VERTICAL&page=2&searchId=c510accc-3740-41e9-98c3-f9ae9dd1212d&sid=kOH

.Extract the following data from each profile:

i. Full Name
ii. Job Title
iii. LinkedIn Profile URL
iv. Status (optional, if available)

4. Database Model:

.Use the following Django model to store scraped data:

class LinkedInProfile(models.Model):
fullName = models.CharField(max_length=250)
jobTitle = models.CharField(max_length=250)
linkedInUrl = models.CharField(max_length=250)
status = models.CharField(max_length=250, null=True)
createdOn = models.DateTimeField(auto_now_add=True)

5. Send Connection Requests:

.Programmatically send a connection request to each scraped profile.

.Include a personalized message when sending the request (message template will be provided or can be hardcoded for now).


Deliverables:
.Django project code with all dependencies and documentation.
.Working scraper with authentication, data extraction, and request sending.
.Instructions for setting up and running the project locally.

Skills Required:
.Python
.Django
.Web Scraping (Selenium, BeautifulSoup, or Playwright)
.Experience with LinkedIn automation (preferred)
.SQLite

Important Notes:
.The solution must handle LinkedIn’s anti-bot mechanisms (like delays, headers, and potential CAPTCHAs).
.Do not use LinkedIn API; this project requires scraping via automated browsing.