ATS Jobs Aggregator — Greenhouse, Lever & Ashby in One Dataset

ATS Jobs Aggregator — Greenhouse, Lever & Ashby Jobs Scraper & API in One Dataset

Keywords: greenhouse jobs scraper · lever jobs API · ashby job board · ATS job listings · job postings API · hiring data · salary data · incremental job monitoring

Get job postings from Greenhouse, Lever and Ashby ATS job boards as one clean, deduplicated dataset — no login, no API key, no proxy setup.

If you track hiring signals, build lead lists, or monitor competitors' headcount, you already know the pain: every company uses a different ATS, every API returns a different shape, and you end up writing three scrapers and a merge script before you get a single usable table.

This Actor does that for you.

What you get

One row per job posting, normalized across all three ATS platforms:

Field	Example
ats	greenhouse
company	Anthropic
title	Account Executive, AI Native
location	New York City, NY; San Francisco, CA
department	Sales
is_remote	true
published_at	2026-10-19T11:17:24-04:00
updated_at	2026-11-08T09:12:00-05:00
url	https://job-boards.greenhouse.io/anthropic/jobs/5456619008
salary	$180,000 - $240,000 (when the ATS exposes it)

Download as JSON, CSV, or Excel straight from the dataset tab.

Sample output

{
  "ats": "greenhouse",
  "company": "Anthropic",
  "job_id": "5456619008",
  "title": "3P Revenue Accounting & Reporting",
  "location": "San Francisco, CA | Seattle, WA",
  "department": "Finance",
  "is_remote": null,
  "published_at": "2026-07-14T18:22:11-04:00",
  "url": "https://job-boards.greenhouse.io/anthropic/jobs/5456619008",
  "salary": "$150,000 - $200,000"
}

How to use it

1. companies — the company slugs from their careers page URL:
   • boards.greenhouse.io/stripe → stripe
   • jobs.lever.co/spotify → spotify
   • jobs.ashbyhq.com/notion → notion
2. ats — pick which platforms to query (default: all three)
3. sinceDays — optional. Set to 1 for a daily scheduled run and only pay for new or changed postings.

That last one matters: schedule this Actor every morning with sinceDays: 1 and you get a cheap daily diff of every company you track, instead of re-pulling everything.

Why this instead of three separate scrapers

• Cross-ATS deduplication built in. Companies migrate between ATS platforms and some run two at once. Duplicate postings are collapsed by company + title + location, so your counts stay honest.
• Normalized schema. Greenhouse returns id as an integer and location as location.name; Lever returns a UUID and a nested categories object; Ashby returns yet another shape. You get one schema.
• No authentication. All three ATS expose public job boards. Nothing to configure, nothing to rotate.
• Failures are visible. Per-company/per-ATS errors are reported rather than silently dropping rows, so you know when a slug is wrong.

Why teams pick this over full-dataset vendors

• Real salary numbers: we regex-extract published ranges from job description bodies — 37.8% coverage vs 0% on the largest competitor's AI-enhanced fields (measured same-day).
• Incremental monitoring: sinceDays + cross-ATS fingerprint dedup means you can schedule daily runs and get only new or changed postings — "new-job alerts for my tracked companies", not a full re-download.
• Honest coverage numbers: we publish measured field coverage instead of claiming completeness.

Typical use cases

• Hiring signal / growth tracking — count open roles per company per week to spot expansion or a freeze
• Lead generation — hiring for a specific stack often means budget; filter by department and stack keywords
• Competitive research — see which teams a competitor is staffing up
• Job board aggregation — feed your own board with fresh, deduplicated postings

Pricing

Pay per job posting returned. Duplicates and out-of-range postings (when using sinceDays) are not billed.

Field coverage (measured on 3,453 postings from 9 companies, Nov 2026 — refreshed daily)

We publish real numbers rather than claiming "complete data":

Field	Filled	Notes
ats, company, job_id, title, location, url	100%	always present
department	100%	from ATS taxonomy, falls back to company metadata
published_at, updated_at	99.9%	drives incremental sinceDays mode
is_remote	~49%	inferred from ATS field, title, or location text
salary	~38%	extracted from job description text where the employer publishes a range (e.g. Anthropic: 77% of postings)
employment_type	96.5%	normalized to a fixed enum (Full-time / Internship / Contract / Part-time / Fellowship / Apprenticeship) via two-stage inference

Employment type is normalized rather than passed through, because each ATS uses different labels (FullTime, Full-time, Permanent, Intern, Internship). Two-stage inference: special types (Intern / Contract / Part-time) are detected from title and description text first; standard roles (engineer, manager, analyst, director…) default to Full-time. You get one consistent enum you can filter on.

Salary is genuinely hard: most ATS boards simply don't have a salary field, so this Actor scans the job description body for phrases like "salary range is $X–$Y" (with $, USD, k, per year/hour variants). Where a company publishes pay ranges, coverage is much higher — e.g. Anthropic: 77% of postings carry a salary range.

If a field matters to your workflow and you need higher coverage, open an issue and tell us which companies you track.

Notes and limits

• Some companies use more than one ATS but not all three — an ATS returning 404 for a slug is expected, not an error (18 of 27 company/ATS combinations in our test).
• Very large boards (e.g. Anthropic with full descriptions, ~10 MB) are read in streamed chunks; the Actor degrades gracefully rather than failing outright.
• Respect each platform's terms and your local regulations when using this data.

Support

Something broken or a company slug missing? Open an issue on the Actor page — response time is tracked publicly, and legitimate data-source changes get fixed fast.