Himalayas Jobs Scraper

Every remote job on Himalayas, in a clean flat schema, with each posting scored for how likely it is to still be open.

Himalayas is one of the better remote job boards — it's the only major one that publishes an explicit expiry date and numeric timezone restrictions on every listing. This Actor uses both, and hands you a dataset you can filter and query rather than a dump you have to clean.

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What each row contains

Identity	stable id across runs, posting URL, direct applyUrl
The role	title, company, plain-text description, tags, employment type, seniority
Where	normalised regions (worldwide / usa / canada / latam / uk / europe / apac / africa / middle_east) plus timezone restrictions as UTC+2-style tokens
Pay	min, max, currency and period as numbers — annualised on request so hourly and monthly rates compare correctly
When	posted date, expiry date, age in days
Trust	freshness 0–1, ghostRisk low/medium/high, and ghostReason explaining the verdict in plain words

The trust fields

Job boards are full of postings that are still published but no longer open. Because Himalayas publishes a real expiry date, the risk scoring here is unusually well-grounded — an expired posting is detected outright rather than inferred from age.

{
  "title": "Senior Backend Engineer",
  "company": "Acme",
  "regions": ["worldwide"],
  "timezoneRestrictions": ["UTC-5", "UTC-4", "UTC-3"],
  "salaryMin": 120000, "salaryMax": 160000,
  "salaryCurrency": "USD", "salaryPeriod": "year",
  "freshness": 0.71,
  "ghostRisk": "low",
  "ghostReason": ["recent, and nothing contradicts it"]
}

Freshness decays on a 21-day half-life. A posting with no date scores 0.5, not 1.0 — absence of evidence is not evidence of freshness.

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Example input

{
  "query": ["golang", "backend", "platform"],
  "regions": ["usa", "europe"],
  "seniority": ["senior", "lead"],
  "maxGhostRisk": "low",
  "salaryOnly": true,
  "maxItems": 200
}

Everything is optional — run it empty and you get the whole board, best-first.

Worth knowing:

• Search terms are OR-ed. ["go", "rust"] returns jobs mentioning either.
• Worldwide jobs match every region filter, because a job open to everyone is open to you.
• minSalary is annualised first, so hourly and monthly rates compare correctly.
• maxItems is your cost ceiling — you're billed per job returned.
• maxPagesPerSource controls depth. Himalayas serves 20 jobs per page and ignores any limit parameter, so 10 pages is roughly 200 jobs and 35 is roughly 700.

Output

One dataset item per job, ordered best-first: lowest ghost-job risk, then freshest. Export as JSON, CSV or Excel, or pull it from the API like any Apify dataset.

Common uses

• Filtering out expired and stale job postings. ghostRisk, ghostReason and freshness say which listings are probably no longer open. Himalayas publishes a real expiry date, so here that judgement is grounded rather than inferred.
• Salary benchmarking and compensation data. salaryMin, salaryMax, salaryCurrency and salaryPeriod are numbers, not strings, and minSalary annualises before comparing — so hourly, monthly and yearly postings line up.
• Timezone-restricted remote hiring. timezoneRestrictions and normalised regions let you keep only the jobs someone in your timezone can actually take, instead of "remote" that means "remote if you live in Berlin".
• Remote job market and hiring data. Run it on a schedule and track how remote hiring, salary ranges, seniority mix and regions move over time.
• Building a job board, job feed or job alerts. id is stable across runs, so diffing today's dataset against yesterday's gives you genuinely new jobs rather than a board reshuffle.
• Recruitment and talent research. Company, title, tags, seniority, employment type and normalised regions on every row.

Export as JSON, CSV or Excel, or read the dataset straight from the Apify API.

How it fetches

Himalayas publishes a public, documented, unauthenticated JSON API and this Actor reads it — paced, budgeted, with an honest user agent. No HTML scraping, no bot-check evasion, no personal data. Job adverts only.

Want more than one board?

Remote Jobs Aggregator runs this same pipeline across six boards at once — Himalayas, Remote OK, Remotive, Arbeitnow, We Work Remotely and Jobicy — folding duplicates and marking which boards carry each job.

Useful thing measured while building it: those six boards barely overlap. Only 1 of 1,513 company+title pairs appeared on more than one. So the aggregator isn't about removing duplication — there almost isn't any — it's about getting six boards' worth of distinct jobs in one query.