ATS Job Postings — Greenhouse, Lever, Ashby & Workday Jobs

ATS job postings in one schema — Greenhouse Lever jobs, Ashby and Workday boards normalized into the same fields.

Every major ATS in one schema — aggregate job postings across Greenhouse, Lever, Workday, Workable and more for full hiring-market coverage.

📊 Sample Output

company	title	department	location	posted_at	updated_at
stripe	Abuse Investigator	8611 Security Analytics	Dublin	2026-09-03T13:32:53-04:00	2026-09-10T13:11:58-04:00
stripe	Abuse Investigator	8611 Security Analytics	Seattle, San Francisco, New York City	2026-09-09T10:50:29-04:00	2026-09-10T13:11:58-04:00
stripe	Abuse Investigator	8611 Security Analytics	Dublin	2026-09-03T13:30:34-04:00	2026-09-12T13:25:25-04:00
stripe	Account Executive, AI Sales	1654 Account Executives (AI)	San Francisco, CA	2026-09-18T13:27:51-04:00	2026-09-18T13:27:51-04:00
stripe	Account Executive, AI Startups - Grower	1653 Startups - Account Executives (NA)	New York	2026-09-15T21:47:18-04:00	2026-09-15T21:47:18-04:00
stripe	Account Executive, AI Startups (Hunter)	1653 Startups - Account Executives (NA)	San Francisco	2026-08-19T14:02:07-04:00	2026-09-10T13:11:58-04:00

Real rows from run uoKiJCI8owv4y7xwD on build 0.0.9 (2026-09-25), unedited apart from masked emails/phones and shortened long text; fields the source does not publish are empty.

⚙️ Sample inputs

This is the Store example input — the same input Apify's automated check runs — 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": [
    "greenhouse:stripe",
    "ashby:ramp",
    "lever:spotify"
  ],
  "keywords": [],
  "maxJobs": 100
}

🔧 Input reference

Field	Type	Default	What it does
companies (required)	array	["greenhouse:stripe", "ashby:ramp", "lever:spotify"]	Career boards to scrape, pulled DIRECTLY from each employer's ATS. Each entry can be: (1) a board URL (we auto-detect the ATS + company) such as https://boards.greenhouse.io/strip…
keywords	array	[]	Optional. Only return jobs whose title contains one of these (case-insensitive) words/phrases, e.g. ['engineer', 'data', 'remote']. Leave empty to return all open roles.
maxJobs	integer	100	Hard cap on the total number of jobs returned across all boards in this run. 0 = no cap (return all matching open jobs).
enrichWorkday	boolean	true	Workday only. When on, each Workday job is enriched with a per-job detail request to fill description, employment type, posted date and req id (slower, more requests). When off, o…
notionConnector	string		Optional. Connect your Notion workspace and the run also writes the jobs as a Notion page - the Actor never sees your Notion credentials. Authorize a Notion MCP connector in Apify…
notionParentId	string		Optional. The Notion page ID to create the page under. If blank, Notion creates a private page in your workspace.
supabaseConnector	string		Optional. Connect your own Supabase project and the results are also inserted into a table in the same run — the Actor never sees your Supabase credentials. Authorize a write-scop…
supabaseTable	string	"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.
supabaseProjectId	string		Your 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/…
deliveryDedup	boolean	true	When on (default), records already delivered in a previous run are not delivered again to the same destination — so scheduled runs only push new items. Turn off to deliver every r…

🧾 JSON sample record

One real record from run uoKiJCI8owv4y7xwD (emails/phones masked, long text shortened):

{
  "company": "stripe",
  "title": "Abuse Investigator",
  "department": "8611 Security Analytics",
  "team": null,
  "location": "Dublin",
  "remote": null,
  "employment_type": null,
  "posted_at": "2026-09-03T13:32:53-04:00",
  "updated_at": "2026-09-10T13:11:58-04:00",
  "job_url": "https://stripe.com/jobs/search?gh_jid=8172508",
  "apply_url": "https://stripe.com/jobs/search?gh_jid=8172508",
  "description": "Who we are \n\nAbout Stripe\n\nStripe 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.\n\nAbout the team\n\nAbuse 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.\n\nWhat you’ll do\n\nYou'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.\n\nResponsibilities\n\n- 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.\n\n- 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.\n\n- 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.\n\n- Streamline incident response capabilities, ensuring the tooling and processes are clear, accurate and efficient\n\n- Work cross-functionally with security, fraud and data science teams to build agentic solutions for responding to abuse incidents at scale\n\n- Effectively communicate cross-functionally with legal and policy teams to assess and mitigate risks, while demonstrating strong problem-solving under pressure.\n\n- Collaborate effectively with teammates, leading projects, mentoring others, and developing and championing quality standards within the team\n\nWho you are\n\nWe'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.\n\nMinimum requirements\n\n- 3+ years of experience conducting incident response in security, product abuse or trust domains\n\n- 3+ years experience analyzing large data sets to solve problems and/or building models with a behavioral approach to fraud detection\n\n- B.S. or M.S. Computer Science or related field, or equivalent experience\n\n- Expert knowledge of Python and SQL, and familiarity with other programming languages\n\n- 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\n\n- Ability to communicate results clearly and focus on impact\n\n- Ability to think creatively and holistically about reducing risk in a complex environment\n\nPreferred qualifications\n\n- An adversarial mindset, understanding the goals, behaviors, and TTPs of threat actors.\n\n- Experience with engineering, data processing and analysis tools (e.g. Databricks, Trino, etc.)\n\n- Familiarity with common open-source frameworks for big data processing and/or data science (PySpark, Pandas, Sci-kit Learn, etc.)\n\n- Experience with tactical threat intelligence and/or hunting for sophisticated threat actors in an enterprise environment\n\n- Ability to proactively challenge the status quo by leveraging data and taking a user-centric approach to address complex product integrity challenges",
  "salary": null,
  "ats_platform": "greenhouse",
  "job_id": "8172508"
}

💰 Pricing

Event	Price (USD)	When it is charged
Actor Start (apify-actor-start)	$0.01	Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event).
Job extracted (apify-default-dataset-item)	$0.05	Charged per job posting extracted and normalized from any supported ATS (Greenhouse, Lever, Ashby, SmartRecruiters, Workday) into your 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 × $0.05 = $5.00 plus the start fee.

🔗 Related Actors

More from the NexGenData Jobs & hiring signals family:

• Company Job Board Scraper — Careers Page Jobs API
• Foundit India Jobs Scraper — Salaries & Skills, No API Key
• Greenhouse Jobs Scraper — Hiring Data API

4 more in this family on the NexGenData Store page.

⏰ Run this as a weekly hiring monitor (recommended)

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

📊 What you get

Clean JSON, one record per job record (full output has 15 fields):

• company — Company
• title — Title
• department — Department
• team — Team
• location — Location
• remote — Remote
• employment_type — Employment type
• posted_at — Posted at
• updated_at — Updated at
• job_url — Job url

Pricing: $0.05 per job record (Pay-Per-Event) — about 20 job records per $1.

🤖 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 — agents can call this actor with USDC, no API key required.

🔗 Related jobs & hiring actors

The ATS family — every major ATS, one schema: Greenhouse · Lever · Workday · Workable & SmartRecruiters · Company Job Board

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

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

Deepest single source — LinkedIn Jobs Scraper: LinkedIn Jobs Scraper

APAC coverage: Singapore MyCareersFuture

Agent front door — Job Market MCP: Job Market MCP

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Public job-posting and hiring-signal data.