🧑‍💻 🧰 ATS Hiring & Closure Delta Feed — Greenhouse, Lever, Ashby

Collect normalized public jobs from six company ATS boards and turn scheduled checks into confirmed openings, changes, closures, and reopenings.

Output is one job_record row per result; billing is pay-per-event, the value event being one job delta (a $0.02 start fee per run, then $0.01 per job delta plus one $0.10 source check per run that reads the source).

No login, no API key and no CAPTCHA solving are involved: the source is read logged-out.

7. Use with:

Agencies maintaining public requisition histories; workforce vendors updating account workflows; investors and researchers preserving public hiring records; data teams replacing six adapters with one scheduled feed.

📊 Sample Output

Real rows from run QYXMEZ8xIx3CtHdvk on build 0.1.24 (2026-09-17), the same input as the Quick start below — every value is as the source published it (emails masked, long text shortened):

run_id	provider	composite_key	title	job_url	apply_url
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|8172510	Abuse Investigator	https://stripe.com/jobs/search?gh_jid=8172510	https://stripe.com/jobs/search?gh_jid=8172510
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|8172508	Abuse Investigator	https://stripe.com/jobs/search?gh_jid=8172508	https://stripe.com/jobs/search?gh_jid=8172508
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|8172487	Abuse Investigator	https://stripe.com/jobs/search?gh_jid=8172487	https://stripe.com/jobs/search?gh_jid=8172487
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|8172503	Abuse Research Engineer	https://stripe.com/jobs/search?gh_jid=8172503	https://stripe.com/jobs/search?gh_jid=8172503
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|7532733	Account Executive, AI Sales	https://stripe.com/jobs/search?gh_jid=7532733	https://stripe.com/jobs/search?gh_jid=7532733
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|8204645	Account Executive, AI Startups - Grower	https://stripe.com/jobs/search?gh_jid=8204645	https://stripe.com/jobs/search?gh_jid=8204645
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|8130725	Account Executive, AI Startups (Hunter)	https://stripe.com/jobs/search?gh_jid=8130725	https://stripe.com/jobs/search?gh_jid=8130725
QYXMEZ8xIx3CtHdvk	greenhouse	greenhouse|Stripe|8077887	Account Executive, Bridge	https://stripe.com/jobs/search?gh_jid=8077887	https://stripe.com/jobs/search?gh_jid=8077887

The run finished with the status message: CAPPED: reason=max_items_cap_reached; observed=12; emitted=10; targets_complete=0/2; capped=[Stripe: 10 of 11 observed; OpenAI: cap reached before any record (0 emitted)]

✅ What you get

One of three record types, discriminated by record_type: job_record, job_delta, or source_receipt.

Each row is flat JSON with these fields (from the dataset schema and the sample run; a field the source does not publish for a given row is null):

• record_type (string) — Discriminator.
• run_id (string) — Apify run id that produced this item.
• provider (string) — ATS provider.
• target_key (string) — Buyer target key (companyName or boardId).
• provider_job_id — e.g. 8172510
• composite_key (string) — provider|target_key|provider_job_id.
• company_name — e.g. Stripe
• title (string/null) — Job title (non-null on job_record).
• job_url (string/null) — Public job URL.
• apply_url — e.g. https://stripe.com/jobs/search?gh_jid=8172510
• locations — e.g. ["Seattle, San Francisco, New York City"]
• location_text — e.g. Seattle, San Francisco, New York City
• country_code — null in every sample row
• workplace_type — e.g. unspecified
• department — e.g. 8611 Security Analytics
• team — null in every sample row
• employment_type — e.g. unknown
• language — null in every sample row
• published_at — null in every sample row
• updated_at — e.g. 2026-09-10T13:11:58-04:00
• application_deadline — null in every sample row
• description_plain — e.g. <h2><strong>Who we are </strong></h2> <h3><stro
• description_html — e.g. <h2><strong>Who we are </strong></h2> <h3><stro
• description_truncated — e.g. False
• salary_min — null in every sample row
• salary_max — null in every sample row
• salary_currency — null in every sample row
• salary_interval — null in every sample row
• salary_text — null in every sample row
• raw — null in every sample row
• raw_truncated — e.g. False
• source_url (string) — Exact public request URL supporting this item.
• observed_at (string) — RFC3339 UTC time after shape validation.
• content_hash (string/null) — SHA-256 over canonical buyer-visible mutable fields.
• delta_type (string/null) — job_delta only.
• changed_fields (array/null) — Sorted changed field names on a changed delta.
• terminal (string/null) — source_receipt only.
• reason_code (string/null) — Stable terminal reason code.
• shape_canary_passed (boolean/null) — Whether the source-shape canary passed for this target.

Every run also writes a RUN_RECEIPT record to its key-value store with the source checks it made and the counts it charged — diagnostics never land in the paid dataset.

⚙️ Sample inputs

1. Quick start — the Store example (this is what the sample above came from)

{
  "targets": [
    {
      "provider": "greenhouse",
      "boardId": "stripe",
      "companyName": "Stripe"
    },
    {
      "provider": "ashby",
      "boardId": "openai",
      "companyName": "OpenAI"
    }
  ],
  "watchMode": false,
  "maxItems": 10,
  "maxConcurrency": 6
}

The sample run charged exactly: 1 × $0.02 actor-start + 10 × $0.003 job-record + 0 × $0.01 job-delta + 0 × $0.10 source-check = $0.05 on the Free tier — snapshot preview rows are delivered unbilled; only the source check and the start fee were charged.

2. A smaller, narrowed run

{
  "targets": [
    {
      "provider": "greenhouse",
      "boardId": "stripe",
      "companyName": "Stripe"
    },
    {
      "provider": "ashby",
      "boardId": "openai",
      "companyName": "OpenAI"
    }
  ],
  "watchMode": false,
  "maxItems": 5,
  "maxConcurrency": 6
}

Caps the run at 5 rows; same billing shape as the sample run above.

3. Watch mode on a schedule

{
  "targets": [
    {
      "provider": "greenhouse",
      "boardId": "stripe",
      "companyName": "Stripe"
    },
    {
      "provider": "ashby",
      "boardId": "openai",
      "companyName": "OpenAI"
    }
  ],
  "watchMode": true,
  "maxItems": 10,
  "maxConcurrency": 6
}

The first run seeds a private baseline and emits zero deltas; every later run emits one row per change, billed at $0.01 each plus the $0.10 source check.

🧾 JSON sample record

One real record from run QYXMEZ8xIx3CtHdvk, exactly as it lands in the dataset (emails masked, long text shortened):

{
  "record_type": "job_record",
  "schema_version": "1.0",
  "run_id": "QYXMEZ8xIx3CtHdvk",
  "provider": "greenhouse",
  "target_key": "Stripe",
  "provider_job_id": "8172510",
  "composite_key": "greenhouse|Stripe|8172510",
  "company_name": "Stripe",
  "title": "Abuse Investigator",
  "job_url": "https://stripe.com/jobs/search?gh_jid=8172510",
  "apply_url": "https://stripe.com/jobs/search?gh_jid=8172510",
  "locations": [
    "Seattle, San Francisco, New York City"
  ],
  "location_text": "Seattle, San Francisco, New York City",
  "country_code": null,
  "workplace_type": "unspecified",
  "department": "8611 Security Analytics",
  "team": null,
  "employment_type": "unknown",
  "language": null,
  "published_at": null,
  "updated_at": "2026-09-10T13:11:58-04:00",
  "application_deadline": null,
  "description_plain": "<h2><strong>Who we are </strong></h2> <h3><strong>About Stripe</strong></h3> <p><span style="font-weight: 400;">Stripe is a financial infrastructure platform for business",
  "description_html": "<h2><strong>Who we are </strong></h2> <h3><strong>About Stripe</strong></h3> <p><span style="font-weight: 400;">Stripe is a financial infrastructure platform for business",
  "description_truncated": false,
  "salary_min": null,
  "salary_max": null,
  "salary_currency": null,
  "salary_interval": null,
  "salary_text": null,
  "raw": null,
  "raw_truncated": false,
  "source_url": "https://boards-api.greenhouse.io/v1/boards/stripe/jobs?content=true",
  "observed_at": "2026-09-17T17:46:09Z",
  "content_hash": "97a6ac30d101d72e9728fa3e12464ce618b1ee682a53e1b48c8da4f437275e6a"
}

🔧 How it works

Transport. Plain HTTPS from the Apify platform, no proxy.

Watch semantics. Snapshot mode (watch_mode: false) returns a preview. Watch mode keys every record on a source-native id, stores a private baseline in a named key-value store, and on each later run emits one delta row per change (new / changed / removed). A blocked fetch is never diffed, so a source outage cannot show up as a mass removal.

Terminal states. A run ends NORMAL, CAPPED (your cap was reached), PARTIAL (something was withheld and the message says what), GENUINE_EMPTY (the source was read and truly had nothing in scope) or BLOCKED (the source refused or changed shape — the run FAILS loud and bills nothing). A zero-row run is never reported as a silent success.

Charging. Each job delta is charged at the moment it is pushed (job-delta); a row that fails to charge is not delivered, so the dataset count always equals the charged count. One source-check is charged per run that actually read the source.

1. What it does

Read buyer-selected public Greenhouse, Lever, Ashby, Recruitee, SmartRecruiters, and Personio boards into one schema. Snapshot mode returns current records. Watch mode seeds a private baseline and returns confirmed later deltas. Receipts separate complete, empty, partial, and blocked checks.

5. Watch mode

The first watch run seeds a private baseline and emits zero deltas. Later scheduled runs emit only confirmed changes. A job is closed only after it is absent for closureConfirmations consecutive eligible runs; partial or blocked checks never close a job. Same job = exact composite key; no fuzzy or cross-provider matching.

9. Compliance

Public logged-out sources only; no wall-defeating; no CAPTCHA/login/paywall workarounds; no candidate data; comply on objection. No login, private/licensed data, rental pricing, or always-on process. Buyers are responsible for lawful use and source terms.

Preview vs Watch mode

• Preview / snapshot (watchMode = false): returns current ATS job postings (billed per job-record). No baseline retained.
• Watch (watchMode = true, default): seeds a private baseline on the first run (zero deltas), then emits only qualified job-delta rows against it. A zero-delta poll charges one source-check per source; active runs bill via job-delta and are not double-charged. source_receipt diagnostics go to the RUN_RECEIPT key-value record and log — never the dataset. Each run charges one actor-start.

What is not done. No login, no cookie or CAPTCHA bypass, no private or personal-account data, no browser automation.

⏰ Set it on a schedule

A single run is a snapshot. The value is the feed: open the actor in Apify Console → Schedules → Create new, add this actor with input 3 above (watch_mode: true), and pick a cadence:

• Daily — 0 7 * * * (07:00 UTC): catch changes within a day.
• Weekly — 0 7 * * 1: a Monday digest.

Each run is independent; keep the same input on the schedule so the results stay comparable. Schedules can also POST results to a webhook (Console → Integrations).

💰 Pricing example

Event	Free	Bronze	Silver	Gold
Actor Start (actor-start)	$0.02	$0.02	$0.02	$0.02
Job record (job-record)	$0.003	$0.0027	$0.0024	$0.002
Job delta (job-delta)	$0.01	$0.009	$0.008	$0.0067
Source check (source-check)	$0.10	$0.09	$0.08	$0.07

Worked at the live Free-tier price (watch mode, one delta per change):

• 8 job deltas: $0.02 start + $0.10 source check + 8 × $0.01 = $0.20
• 25 job deltas: $0.02 start + $0.10 source check + 25 × $0.01 = $0.37
• 1000 job deltas: $0.02 start + $0.10 source check + 1000 × $0.01 = $10.12

A run that delivers zero rows charges the $0.02 start fee plus the $0.10 source check (the source was read and reported empty). A BLOCKED run (source refused) fails loud and charges no value event. The start fee is charged once per GB of run memory; the default run memory is 512 MB.

Yield on the sample run: CAPPED: reason=max_items_cap_reached; observed=12; emitted=10; targets_complete=0/2; capped=[Stripe: 10 of 11 observed; OpenAI: cap reached before any record (0 emitted)]. maxItems is a hard ceiling on what is delivered and billed, never a target.

⚖️ Legal & ToS

This actor reads public data only. It collects only what the source publishes to any visitor, identifies itself, and does not access accounts, private data or anything behind authentication. Use the output in line with the source's terms and your local law; the intended use is B2B research and monitoring.

❓ FAQ

Q: Do I need an API key or a login?
A: No. the input schema has no key field and the actor carries no secrets.

Q: Why did my run return 0 rows?
A: Read the run's status message. GENUINE_EMPTY means the source was read and had nothing in scope for your input (in watch mode the first run always seeds the baseline and returns zero deltas); BLOCKED means the source refused and the run failed without billing a value event — retry later or narrow the input. A zero-row run bills the start fee plus one source check.

Q: How many rows can one run return?
A: Up to maxItems (default 1000). Raise the cap for a bigger run; you pay per delivered row.

Q: What does watch mode remember between runs?
A: A private baseline of source-native record ids in a named key-value store. Each later run diffs the live source against it and emits only changes.

Q: How fresh is the data?
A: Every run reads the source live at run time; nothing is cached between runs except the watch baseline. Put it on a schedule for a continuous feed.

Q: What formats can I export?
A: The dataset downloads as JSON, CSV, Excel, XML or RSS from the run's Dataset tab or the Apify API, and any run can push to a webhook or integration.

Q: How is this different from the other ATS job feeds actors?
A: Same output shape and billing model; this one covers 🧰 ATS Hiring & Closure Delta Feed — Greenhouse, Lever, Ashby. The siblings under Related Actors cover the other sources or slices — run several on one schedule for a combined feed.

🆘 Troubleshooting

• Run FAILED with BLOCKED → the source refused the request or changed its page shape → nothing was billed beyond the start fee; retry after a while, and if it persists open an Issue with the run id.
• Status says CAPPED → your cap (maxItems) was reached → raise it for a bigger run.
• Input validation error on start → a field is outside the schema's allowed values → start from the Quick start block and change one field at a time.
• Run TIMED-OUT → a very wide request on a slow day → raise the run timeout in Run options or narrow the input; what was delivered before the timeout is still in the dataset.
• First watch run shows no deltas → expected: it seeds the baseline; changes appear from the second run.

🔗 Related Actors

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• 🏢 About NexGenData — NexGen Watch is NexGenData's fleet of 256 public monitoring and lookup actors built on official sources, pay-per-result. Browse the catalog at apify.com/nexgenwatch.

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