LEAD GENERATION

Career-Page Tooling-Signal Harvester

Crawls target-account career pages on a schedule, extracts new job postings that mention tools you compete with or complement, scores fit, and writes qualified buying signals…

CategoryLead Generation
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires
  • ActionCrawl target career pagesFirecrawl
  • LogicKeep only new postings vs last run
  • ActionExtract roles and score fitOpenAI
  • LogicFilter below fit threshold
  • OutputUpsert signals into HubSpotHubSpotHubSpot

What it does

This workflow watches the career pages of a list of target accounts and turns new role openings into ranked buying signals. A new posting for, say, a 'Senior RevOps Analyst (Salesforce)' tells you an account is investing in a category your product touches. It crawls each page, isolates fresh postings, scores them against your ideal-fit rules, and pushes the strong ones into HubSpot so reps see them in their pipeline.

When to use it

Use it when your buyers reveal intent through hiring rather than ad clicks. Ideal for sellers of dev tools, data platforms, and ops software where a new headcount in a specific function signals an active budget and an imminent tooling decision.

How it works

  1. 1A daily schedule fires the run.
  2. 2Firecrawl crawls each target account's careers URL and returns structured job listings.
  3. 3A logic step diffs against the last run to keep only postings that are genuinely new.
  4. 4An OpenAI step extracts role, seniority, and named tools, then scores fit 0-100 against your rules.
  5. 5A logic gate drops anything below the threshold.
  6. 6Qualified signals are upserted to HubSpot as contact/company notes with the score and source link.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FirecrawlCrawl, scrape, structured extract.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect HubSpotCRM, deals, marketing, support.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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