Greenhouse Jobs Scraper β€” Hiring Data API avatar

Greenhouse Jobs Scraper β€” Hiring Data API

Pricing

from $50.00 / 1,000 job postings

Go to Apify Store
Greenhouse Jobs Scraper β€” Hiring Data API

Greenhouse Jobs Scraper β€” Hiring Data API

Scrape live Greenhouse-hosted job boards by company slug β€” title, location, departments, offices, posted date, full description HTML, application URL. LinkedIn-Jobs alternative for sourcing tools, ATS-competitive intel, sales/BD prospecting target companies, recruiters tracking talent moves.

Pricing

from $50.00 / 1,000 job postings

Rating

0.0

(0)

Developer

NexGenData

NexGenData

Maintained by Community

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0

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2

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0

Monthly active users

2 hours ago

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Greenhouse Jobs Scraper

Scrape open roles from any Greenhouse-powered careers page β€” competitor hiring data and job postings in one clean schema.

πŸ“Š Sample Output

6 real rows delivered by Greenhouse Jobs Scraper β€” Hiring Data API β€” run yOkHJByQGeUBC5lTJ on build 0.0.17

idinternal_job_idrequisition_idcompanycompany_slugtitle
81725083537062See Opening IDStripestripeAbuse Investigator
81725103537063See Opening IDStripestripeAbuse Investigator
81724873537052See Opening IDStripestripeAbuse Investigator
81380443522152See Opening IDStripestripeAccount Executive, AI Sales
82046453550975See Opening IDStripestripeAccount Executive, AI Startups - Grower
81307253520748See Opening IDStripestripeAccount Executive, AI Startups (Hunter)

Real rows from run yOkHJByQGeUBC5lTJ on build 0.0.17 (2026-09-25), unedited apart from masked emails/phones and shortened long text; fields the source does not publish are empty.

βš™οΈ Sample inputs

This is the Store example input β€” the same input Apify's automated check runs β€” so it is proven to return rows on the current build. Paste it into the input form, or send it through the API, CLI or a schedule:

{
"companies": [
"stripe",
"airbnb",
"anthropic"
],
"maxJobs": 100,
"includeContent": true
}

πŸ”§ Input reference

FieldTypeDefaultWhat it does
companies (required)array["stripe", "airbnb", "anthropic"]List of Greenhouse-hosted companies. Accepts either the slug (path segment in https://boards.greenhouse.io/{slug}) OR the company name β€” the scraper normalizes names to slug can…
maxJobsinteger100Maximum total number of job postings to return across all companies (1–5000). The default 100 is a safe smoke-test size; raise for full sweeps.
includeContentbooleantrueIf checked, includes the full job description HTML on each row (~5–30 KB per job). Uncheck for slim metadata-only output.
notionConnectorstringOptional. Connect your Notion workspace and the leads are also written as a Notion page in the same run β€” the Actor never sees your Notion credentials. Authorize a Notion MCP conn…
notionParentIdstringOptional. The Notion page ID to create the page under. If blank, Notion creates a private page in your workspace.
supabaseConnectorstringOptional. Connect your own Supabase project and the results are also inserted into a table in the same run β€” the Actor never sees your Supabase credentials. Authorize a write-scop…
supabaseTablestring"nexgendata_leads"Target table in the public schema (created if missing, with id, a jsonb data column, and a scraped_at timestamp). Default: nexgendata_leads.
supabaseProjectIdstringYour Supabase project reference (e.g. abcdefghijklmnop). Required unless your connector URL is already project-scoped. Find it in your project URL: supabase.com/dashboard/project/…
deliveryDedupbooleantrueWhen on (default), records already delivered in a previous run are not delivered again to the same destination β€” so scheduled runs only push new items. Turn off to deliver every r…

🧾 JSON sample record

One real record from run yOkHJByQGeUBC5lTJ (emails/phones masked, long text shortened):

{
"id": 8172508,
"internal_job_id": 3537062,
"requisition_id": "See Opening ID",
"company": "Stripe",
"company_slug": "stripe",
"title": "Abuse Investigator",
"location": "Dublin",
"departments": [
"8611 Security Analytics"
],
"department_ids": [
81946
],
"offices": [
"Ireland Locations"
],
"office_locations": [
null
],
"absolute_url": "https://stripe.com/jobs/search?gh_jid=8172508",
"first_published": "2026-09-03T13:32:53-04:00",
"updated_at": "2026-09-10T13:11:58-04:00",
"language": "en",
"application_deadline": null,
"metadata": null,
"custom_questions": null,
"source": "greenhouse.io",
"content_html": "<h2><strong>Who we are </strong></h2>\n<h3><strong>About Stripe</strong></h3>\n<p><span style="font-weight: 400;">Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.</span></p>\n<h3><strong>About the team</strong></h3>\n<p>Abuse Operations is the front-line incident response and remediation function handling active product abuse and fraud impacting Stripe and its merchants. This multi-disciplinary group, spanning Incident Managers, Investigators, Forward Deployed Security Engineers, and Data Scientists, neutralizes active attacks, gathers requirements for operational tooling, and leads incidents. The team  works directly with impacted merchants to resolve technical incidents and policy abuse rapidly. Operating primarily across Eastern, Pacific and Western European time zones, these team members regularly coordinate with global stakeholders across the world.</p>\n<h2><strong>What you’ll do</strong></h2>\n<p>You'll play a critical role in safeguarding our financial ecosystem by investigating high-risk accounts and identifying complex patterns of fraud during incidents. You will lead incident response for product abuse and fraud events, conducting deep-dive analyses to identify root causes. By collaborating cross-functionally, you will drive improvements that enhance our fraud detection and prevention strategies at scale. Your expertise will be essential in automating response processes through agentic approaches, allowing us to safeguard merchants and neutralize threats with speed and precision.</p>\n<h3><strong>Responsibilities</strong></h3>\n<ul>\n<li>Investigate, mitigate, and remediate urgent fraud incidents (e.g., ATO, card testing), utilizing FT3-mapped detection and signals enrichment to reduce uncertainty and accelerate response.</li>\n<li>As part of incidents, analyze high-risk accounts to identify fraudulent merchants, card testing, account takeovers, and other fraud vectors, classifying them using FT3 (Fraud Taxonomy 3.0) to standardize threat intelligence.</li>\n<li>Lead incident root cause analyses to identify gaps in current systems and strategies, leveraging the FT3 framework, data-driven model to drive enhancements and process improvements for emerging fraud risks.</li>\n<li>Streamline incident response capabilities, ensuring the tooling and processes are clear, accurate and efficient</li>\n<li>Work cross-functionally with security, fraud and data science teams to build agentic solutions for responding to abuse incidents at scale</li>\n<li>Effectively communicate cross-functionally with legal and policy teams to assess and mitigate risks, while demonstrating strong problem-solving under pressure.</li>\n<li>Collaborate effectively with teammates, leading projects, mentoring others, and developing and championing quality standards within the team</li>\n</ul>\n<h2><strong>Who you are</strong></h2>\n<p><span style="font-weight: 400;">We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.</span></p>\n<h3><strong>Minimum requirements</strong></h3>\n<ul>\n<li>3+ years of experience conducting incident response in security, product abuse or trust domains</li>\n<li>3+ years experience analyzing large data sets to solve problems and/or building models with a behavioral approach to fraud detection</li>\n<li>B.S. or M.S. Computer Science or related field, or equivalent experience</li>\n<li>Expert knowledge of Python and SQL, and familiarity with other programming languages</li>\n<li>Existing experience with log analysis (e.g. first or third party applications, system / data access, event logs), network security, digital forensics, and incident response investigations</li>\n<li>Ability to communicate results clearly and focus on impact</li>\n<li>Ability to think creatively and holistically about reducing risk in a complex environment</li>\n</ul>\n<h3><strong>Preferred qualifications</strong></h3>\n<ul>\n<li>An adversarial mindset, understanding the goals, behaviors, and TTPs of threat actors.</li>\n<li>Experience with engineering, data processing and analysis tools (e.g. Databricks, Trino, etc.)</li>\n<li>Familiarity with common open-source frameworks for big data processing and/or data science (PySpark, Pandas, Sci-kit Learn, etc.)</li>\n<li>Experience with tactical threat intelligence and/or hunting for sophisticated threat actors in an enterprise environment</li>\n<li>Ability to proactively challenge the status quo by leveraging data and taking a user-centric approach to address complex product integrity challenges</li>\n</ul>"
}

πŸ’° Pricing

EventPrice (USD)When it is charged
Actor Start (apify-actor-start)$5e-05Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event).
Job posting (apify-default-dataset-item)$0.05Single Greenhouse job posting pushed to the dataset.

Pay-per-event: you pay only for what the run delivers. A run that delivers nothing bills no result events (only the actor-start event, when the actor defines one). Example: a run that delivers 100 results costs 100 Γ— $0.05 = $5.00 plus the start fee.

More from the NexGenData Jobs & hiring signals family:

1 more in this family on the NexGenData Store page.

Point it at a watchlist of companies and schedule a weekly sweep (0 8 * * 1). New roles become an always-on hiring-signal feed β€” see who's scaling which teams, week over week, before it's obvious.

πŸ“Š What you get

Clean JSON, one record per job listing (full output has 19 fields):

  • id β€” Id
  • internal_job_id β€” Internal job id
  • requisition_id β€” Requisition id
  • company β€” Company
  • company_slug β€” Company slug
  • title β€” Title
  • location β€” Location
  • departments β€” Departments
  • department_ids β€” Department ids
  • offices β€” Offices

Pricing: $0.050 per job listing (Pay-Per-Event) β€” about 20 job listings per $1.

πŸ€– Use with AI agents

Point Claude, the OpenAI Agents SDK, an n8n flow or any MCP-aware client at it and pull hiring data on demand.

Agentic payments (x402): Supports agentic payment via x402 β€” agents can call this actor with USDC, no API key required.

The ATS family β€” every major ATS, one schema: Lever Β· Workday Β· Workable & SmartRecruiters Β· ATS Job Aggregator Β· Company Job Board

Other job sources: HN Who's Hiring Β· We Work Remotely Β· H-1B Salary Search

Hiring-intelligence layer (signals): Hiring Signal Detector Β· Hiring Signals to Notion Β· Tech Hiring Signals

Deepest single source β€” LinkedIn Jobs Scraper: LinkedIn Jobs Scraper

APAC coverage: Singapore MyCareersFuture

Agent front door β€” Job Market MCP: Job Market MCP


Public job-posting and hiring-signal data.