- Job Seekers: Individuals looking for fresh job opportunities in real-time across various locations and industries. - Sales Professionals: Those aiming to identify companies that are actively hiring, potentially indicating growth and sales opportunities. - Recruiters: Professionals seeking to fill positions quickly with filtered job data. - Data Analysts: Users interested in leveraging job market data for analysis and insights.
Problem Solved
Problem Solved
This workflow addresses the challenge of efficiently scraping and organizing job postings from LinkedIn. It automates the process of: - Gathering Relevant Job Data: Users can specify criteria such as location, keywords, and job types to receive tailored job listings. - Real-time Updates: It ensures users receive the most recent job postings, enhancing their chances in the job market. - Data Organization: Job postings are automatically cleaned and organized into Google Sheets, making it easy to track applications and opportunities.
Workflow Steps
Workflow Steps
1. User Input: The workflow begins when a user submits a form with details such as Job Location, Keyword, and Country. 2. API Call to Bright Data: This triggers an API call to Bright Data to start scraping job postings based on the provided criteria. 3. Polling for Data Availability: The workflow waits and periodically checks the status of the data scraping process to see if the data is ready. 4. Data Retrieval: Once the data is ready, another API call retrieves the job postings from Bright Data. 5. Data Cleanup: The workflow processes the retrieved data to remove HTML tags and flatten nested fields for easier readability. 6. Data Storage: Finally, the cleaned job data is appended to a Google Sheet, allowing users to access and manage their job search efficiently.
Customization Guide
Customization Guide
- Adjust Filters: Users can modify the filters in the HTTP Request node to customize the job search criteria, such as changing location, keyword, or job type. - Polling Interval: If Bright Data is slow, users can change the polling interval in the Wait node to optimize the waiting time. - Data Mapping: Users can customize how data is organized in Google Sheets by modifying the columns in the Google Sheets node, ensuring that it meets their specific needs. - Additional Logic: Users can add custom logic in the Code node to prioritize or score job listings based on their preferences.