This article provides a complete, practical walkthrough of the Wait Splitout Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automated Prospecting: Scraping Glassdoor Jobs with Bright Data, AI & Google Sheets via n8n
   
   Meta Description:  
   Discover how to automate job prospecting using an advanced n8n workflow that scrapes Glassdoor job listings via Bright Data, uses AI to generate personalized pitches, and stores everything neatly in Google Sheets.
   
   Keywords:  
   n8n workflow, job scraping automation, Bright Data API, Glassdoor job listings, Google Sheets automation, AI job prospecting, B2B lead generation, automated outreach, OpenAI GPT integration, content repurposing
   
   Third-Party APIs Used:
   
   - Bright Data API – for scraping and retrieving job listing data from Glassdoor
   - Google Sheets API – for storing and updating job post data and AI-generated pitches
   - OpenAI (GPT-4) – for generating personalized outreach messages based on job descriptions
   
   ---
   
   Article:
   
   # Automating Job Prospecting: Scrape Glassdoor Listings and Generate AI-Powered Outreach with n8n, Bright Data, and OpenAI
   
   For B2B marketers, recruiters, or agencies offering content or consulting services, scraping job listings from platforms like Glassdoor can uncover prime leads. But manually collecting jobs, reading descriptions, and tailoring outreach pitches is time-consuming.
   
   What if you could automate all of it—from sourcing job data to AI-crafted pitches—at scale?
   
   In this article, we break down an intelligent n8n workflow that does just that. Built using Bright Data, Google Sheets, and OpenAI's GPT models, this automation enables users to:
   
   - Search for jobs by location, keyword, and country
   - Scrape and structure job and company data from Glassdoor
   - Automatically generate personalized cold outreach sentences using AI
   - Store everything neatly inside a Google Sheet for further action
   
   Let’s dive into how it works.
   
   ---
   
   ## Step 1: Starting the Workflow with a Job Discovery Form
   
   At the front of the workflow is a form trigger, titled **“LinkedIn High Intent Prospects And Job Post Hunt.”** This is where you input the parameters for the job search:
   
   - Job location (e.g., “New York”)
   - Keyword (e.g., “CMO,” “AI Architect”)
   - Country code (e.g., US, UK)
   
   Once submitted, the workflow sends a POST request to the Bright Data API to initiate a new job listing snapshot based on your query.
   
   ---
   
   ## Step 2: Calling Bright Data to Scrape Glassdoor Jobs
   
   Using the HTTP Request node, the workflow sends a request to the Bright Data dataset API. Here's what it includes:
   
   - The dataset ID from Bright Data (for Glassdoor jobs)
   - Your selected filters (location, keyword, country)
   - Additional parameters to specify type and formatting
   
   This triggers Bright Data to start assembling relevant listings. A snapshot ID is returned to track the scraping process.
   
   ---
   
   ## Step 3: Polling and Waiting Until Data Is Ready
   
   To avoid pulling incomplete data, the workflow includes a Wait Node followed by a Status Check node. These repeatedly poll Bright Data for the snapshot’s readiness.
   
   Once the snapshot status changes from “running” to “done,” the workflow proceeds.
   
   ---
   
   ## Step 4: Retrieving the Scraped Data and Storing It in Google Sheets
   
   With the data ready, another HTTP request fetches all the results in JSON format. This includes:
   
   - Job title, location, company info
   - Reviews and ratings
   - Salary estimates (when available)
   
   These get appended into a structured Google Sheet using the Google Sheets API. A helpful public-ready template for the sheet is linked here.
   
   ➡️ Google Sheets Template: [Get the Template](https://docs.google.com/spreadsheets/d/1ZYRk83hNIQCyQNaKpchdnbTiapVxE4aG6ZFIQlwEoWM/edit?usp=sharing)
   
   ---
   
   ## Step 5: Leveraging OpenAI to Create Personalized Outreach
   
   This is where it gets really smart.
   
   The job listings are split into individual records. The workflow examines three key fields:
   
   - Company name
   - Job title
   - Job description
   
   That data is then passed through a LangChain-powered LLM integration. The AI performs a logical task:
   
   “If this job relates to marketing, content creation, or audience engagement…  
   Then write 1–2 concise icebreaker sentences that hint at how a content repurposing service might help.”
   
   Otherwise, the AI simply returns:  
   “---JOB POST NOT RELEVANT---”
   
   This ensures only relevant jobs receive personalized messages—and no time is wasted.
   
   Example AI Output:
   
   > Hey,  
   > I noticed you're hiring for a Content Marketing Manager.  
   >  
   > We’ve helped similar teams repurpose existing blog and video content into new formats like social clips and newsletters.  
   > Thought this might be valuable for your growth efforts.
   
   Genius? We think so.
   
   ---
   
   ## Step 6: Saving AI-Generated Pitches Back into the Google Sheet
   
   The final piece of the puzzle involves writing the AI-generated messages back into the original Google Sheet alongside each job record.
   
   This keeps both prospect data and pitch content in one place, ready for action. You could take it further by connecting this sheet to a CRM or sending personalized emails via another n8n webhook.
   
   ---
   
   ## Why This Workflow Works
   
   What makes this automation powerful is not just its technical complexity—but its strategic relevance:
   
   - You don’t pitch blindly. You respond to companies already hiring for skills that align with your services.
   - You're leveraging AI not to replace creativity but to scale rational, tailored communication.
   - You centralize all actions in one tool: n8n.
   
   This solution is perfect for growth hacking, B2B sales, freelance consultants, and agency teams. Plus, with Google Sheets as the data layer, anyone on the team can easily access the results.
   
   ---
   
   ## Final Thoughts: Building Smarter, Not Harder
   
   If you're looking to scale your outreach, find higher-quality leads, and use AI in a way that feels natural (and not spammy!), this n8n workflow is a prime example of building smarter automation.
   
   And with open integrations like those from Bright Data, OpenAI, and Google Sheets, the limits are only bound by your imagination.
   
   —
   
   📧 Need Help?
   For support or customization, contact Yaron Been at yaron@nofluff.online or check out his tutorials on [YouTube](https://www.youtube.com/@YaronBeen/videos) and [LinkedIn](https://www.linkedin.com/in/yaronbeen/).
   
   🧠 Documentation & Setup Links:
   - Bright Data Docs: https://docs.brightdata.com/introduction
   - Get Sheet Template: https://docs.google.com/spreadsheets/d/1ZYRk83hNIQCyQNaKpchdnbTiapVxE4aG6ZFIQlwEoWM/edit
   
   Start prospecting smarter today 🚀
   
   --- 
   
   End of Article.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.