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Library / Data Sync & ETL

Data Sync & ETL

Track new LinkedIn jobs daily in Google Sheets with Apify MEKA

When you run it, it moves and syncs data using Google Sheets, Apify.

What it does

## Before running Who it is for: Recruiters, job seekers, researchers, and teams tracking one public LinkedIn search in a Google Sheet. How it works: The workflow runs the MEKA LinkedIn Jobs Scraper through Apify, verifies Actor OUTPUT, reads existing IDs, and appends only unseen public jobs. The Manual Trigger supports a supervised first run. The daily Schedule Trigger stays inactive until enabled. Setup: 1. Create a Google Sheet with a Jobs worksheet. Put these exact headers in A1: id, title, company, location, postedAt, url, salary, employmentType, seniority, firstSeenAt. 2. In Search settings, enter your spreadsheet document ID and adjust keywords, location, limit, and budget. 3. Create HTTP Header Auth named Authorization with value Bearer and select it on Start Actor, Poll Actor Run, Get Run Output, and Get Dataset Items. 4. Connect Google Sheets OAuth2 to Read Existing Jobs and Append Rows. 5. In Workflow settings -> Timezone, choose Etc/UTC or your local timezone, then verify what the Daily Schedule's 08:00 means. Imports can use the n8n instance default even when the JSON declares UTC. 6. Save, run the Manual Trigger, and inspect the execution and sheet. Enable the schedule only after that test succeeds. Requirements: an Apify token, a Google account that can edit the sheet, and n8n credentials for both services. This JSON contains no credentials or private spreadsheet ID. Customize: change the search, date window, limit, budget, worksheet name, or schedule. A result limit is not full-market coverage. Repository: https://github.com/meka-im/linkedin-jobs-scraper-examples MEKA Actor: https://apify.com/meka.im/linkedin-jobs-scraper

The steps

  1. Manual Trigger
  2. Daily Schedule
  3. Search settings
  4. Input Validation
  5. Start Actor
  6. Assess Start Run
  7. Is Run Succeeded?
  8. Wait for Actor
  9. Poll Actor Run
  10. Assess Polled Run
  11. Get Run Output
  12. Assess Run Output
  13. Get Dataset Items
  14. Normalize Jobs

20 steps in total.

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