datagrit › Jobs & salaries › Greenhouse Salary Scraper - Job Pay Ranges

Greenhouse Salary Scraper - Job Pay Ranges

Greenhouse job postings with published pay ranges by zone, annualised pay, filters and new-since-last-run detection.

Greenhouse Salary Scraper reads the public job boards of companies that hire through Greenhouse and returns every open posting as a clean record together with its published pay ranges. You get the range for every location pay zone, the overall band, the currency, the pay period and yearly amounts, plus flags for equity, bonus and commission mentioned in the pay text. Filter by minimum annual pay, currency, title keywords, department, location and posting age, and export the data as JSON, CSV or Excel, call it through the Apify API, or plug it into n8n, Make and AI agents through MCP.

Who is it for?

• Recruiters and sourcers who want a feed of new roles at target companies, filtered by title and location.
• Job seekers and career coaches who compare pay bands across companies before negotiating.
• Compensation and HR analysts who benchmark pay by role and location zone, including Canadian and European zones of US employers.
• Sales teams who use hiring as a buying signal and want to see which companies are opening roles.

How to use it

1. Enter the companies you want as Greenhouse board names or URLs. The name is the last part of the careers link: boards.greenhouse.io/robinhood means the name is robinhood.
2. Add filters if you need them: title keywords, department, location, only postings with a pay range, minimum annual pay, pay currency, or how recently the posting was first published.
3. For a recurring feed, switch on Only postings new since my last run and schedule the Actor. Each run returns only the postings that earlier runs with the same companies and filters have not delivered yet.
4. Download the dataset or fetch it from the API.

The Actor reads each board in a single request that already contains the pay ranges, so pay does not slow the run down. On 30 September 2026 a run over five boards (Robinhood, Airbnb, Coinbase, Mercury and Discord) returned all 640 open postings in 10 seconds, 543 of them with a published pay range.

What data do you get?

Each record contains the title, departments, offices and location, the requisition number, first publication and update dates, the age of the posting in days and its link. When pay is published you also get the lowest and highest amounts across the pay zones, the currency of that band and the list of all currencies used, the pay period, the yearly equivalents, the number of pay zones, a one-line summary of every zone and flags for equity, bonus and commission mentioned in the pay text. The plain-text description is available on request.

Pay zones in several currencies

Many US employers publish a Canadian or European zone next to the US ones. Every zone is kept in the summary and counted in payZones. The band (payMin, payMax and the yearly amounts) is computed in one currency, because amounts are never converted between currencies: the first currency from your Pay currencies filter that the posting uses, or the currency of the first zone when you do not filter by currency. On 30 September 2026, 38 of Mercury's 63 postings had a zone in CAD after the USD zones; filtering by CAD returns all 38 with the band in CAD.

The same applies to Minimum annual pay: without a currency filter the threshold is compared with each posting in its own currency, so on a global board 150000 also keeps postings paying 150,000 INR or PHP a year. Set Pay currencies together with Minimum annual pay, for example USD with 150000, to compare like with like.

Pay period

Greenhouse does not publish a pay period field, so the period is worked out from the title of the range, for example Hourly Rate, and from the size of the amount. When it cannot be determined the period and the yearly amounts are left empty instead of guessed.

Is it legal to scrape this data?

The Actor reads only the public job board API that Greenhouse offers to employers for publishing their open roles. It does not log in, bypass access controls or collect candidate data. Job descriptions can contain personal data such as a recruiter name, so if you request them you are responsible for handling them in line with applicable data protection law. This description is not legal advice.

Output fields

Every result is one flat record, so it drops straight into a spreadsheet, a database or a CRM.

Sample record

{
  "company": "robinhood",
  "id": "7563040",
  "title": "Senior Software Engineer",
  "departments": "Financial Crimes",
  "offices": "Denver, CO; New York, NY",
  "location": "Denver, CO; New York, NY; Westlake, TX",
  "requisitionId": "JR109304",
  "firstPublishedAt": "2026-09-03T15:43:18.000Z",
  "updatedAt": "2026-09-25T17:03:21.000Z",
  "daysSincePublished": 27,
  "jobUrl": "https://boards.greenhouse.io/robinhood/jobs/7563040",
  "hasPay": true,
  "payMin": 148000,
  "payMax": 198000,
  "payCurrency": "USD",
  "payCurrencies": "USD, CAD",
  "payPeriod": "year",
  "payAnnualMin": 148000,
  "payAnnualMax": 198000,
  "payZones": 3,
  "payRangeSummaries": "Zone 1: 148K-198K USD/year | Canada: 170K-220K CAD/year",
  "equityMentioned": true,
  "bonusMentioned": true,
  "commissionMentioned": false,
  "descriptionText": null,
  "found": true,
  "scrapedAt": "2026-09-30T08:00:00.000Z"
}

Input

Call it from your code

Run the Actor and get the results in one request. Replace YOUR_APIFY_TOKEN with the token from your Apify account settings.

curl -X POST "https://api.apify.com/v2/acts/datagrit~greenhouse-salary-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"companies":["robinhood"],"onlyWithPay":true,"maxItems":50}'

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('datagrit/greenhouse-salary-scraper').call({
  "companies": [
    "robinhood"
  ],
  "onlyWithPay": true,
  "maxItems": 50
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.length, items[0]);

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("datagrit/greenhouse-salary-scraper").call(run_input={
  "companies": [
    "robinhood"
  ],
  "onlyWithPay": True,
  "maxItems": 50
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(len(items), items[0] if items else None)

Frequently asked questions

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