Linkedin Job Posting parser

The job required is Initial Data Collection. The primary focus will be on extracting IT job postings from LinkedIn, with an initial goal of gathering 1000 records.

The tool we offer should be easily reproducible and user-friendly for future projects. This will help our client gather data and analyze results for POC of his project.

We received a request for specific data structure. The task description outlined exactly what data was needed.

An important point here is that it's LinkedIn, which doesn't make it easy to gather data. We had to think outside the box. However, instead of rushing into it, we first did some preliminary work to offer the best solution. We checked if there were existing solutions on the market for data collection and if they could solve our problem. We verified if the LinkedIn API was accessible and could address the issue.

As a result, a scrapper became the only option to solve the problem.

At the start of the project, our primary focus is on collecting a wealth of data from job postings. We aim to create a database using Linkedin, and extract all the essential information needed. It's important to ensure that we provide the precise details that the client is seeking.

 This led us to perform identification - to determine if the post meets the initial selection criteria.

Next, we needed to work on the test. In other words, extracting specific information from job descriptions that interests the client. This includes Software Stack, Qualifications Stack, Activities Stack, and Hardware Stack.

Once the parser is created, it is essential to deploy it onto the client server. Our main objective is to assist in the setup and provide guidance on usage.

The client should feel confident in utilizing it independently.

The client receives the necessary data to carry on their research, enabling them to make informed managerial decisions.

Additionally, a parser deployed on the client's server can be utilized to conduct repeatable research.