LinkedIn Job Alerts in n8n: Apify to Data Table + Slack

Get alerted instantly when new LinkedIn jobs match your targets—automatically in n8n

This n8n workflow monitors selected companies for new LinkedIn job postings using Curly’s Apify Actor, deduplicates results, stores unseen roles in an n8n Data Table, and (optionally) sends a Slack digest per company—so you always have fresh hiring signals without manual checking.

What this workflow does

• Runs on a schedule (every 12 hours) or can be started manually to check for new LinkedIn job postings.
• Calls the Apify actor: sends your configured company slugs/LinkedIn company URLs (plus optional filters like jobTitles, locations, and limits) to curly/linkedin-jobs-scraper and retrieves the scraped dataset items.
• Normalizes into a consistent job schema (including job ID, company, title, location, relevant URLs, and timestamps such as published/scraped times).
• Deduplicates within a run by removing duplicate job IDs returned in the same scrape.
• Checks an n8n Data Table to keep only jobs you haven’t stored before.
• Persists new hiring signals by inserting newly detected job records into a reusable n8n Data Table (the provided target name is curly_hiring_signals).
• Optional Slack notifications: groups newly stored jobs by company and posts one digest message per company to a Slack channel.

Use cases

• Track a shortlist of SaaS companies and surface new LinkedIn roles for recruiting, sales intelligence, or partnership outreach.
• Build a lightweight “hiring CRM” by storing first-seen job records (with job IDs and timestamps) for later review.
• Notify a team in Slack only when new jobs appear for specific companies—no duplicate spam.

Technical details

• Nodes / building blocks: apifyn8n-nodes-apify (Apify), apify via Apify credential, data table, code (normalization/dedup logic), set, slack (digest), and sticky note for workflow guidance.
• Data model: create an n8n Data Table named curly_hiring_signals with columns including job_id, job/company details, and date fields such as published_at, scraped_at, and first_seen_at.
• Configuration: update target company slugs/LinkedIn company URLs and optional filters in the workflow’s target configuration steps.