Himalayas Jobs Scraper

Extract remote job listings from Himalayas into clean, analysis-ready datasets. Collect job titles, companies, locations, compensation details, work type, requirements, and application deadlines at scale. This scraper is built for recruiting teams, talent intelligence workflows, and job market research.

Features

• Flexible search inputs — Run by keyword and location, or a single startUrl.
• Rich job coverage — Collect both listing-level and detailed job metadata in one dataset.
• Automatic pagination — Traverse result pages up to your configured limits.
• Null-clean output — Empty fields are removed automatically for cleaner downstream processing.
• Duplicate protection — Canonical URL deduplication avoids repeated job records.
• Integration-ready data — Export clean records for dashboards, CRMs, ATS tools, and automations.

Use Cases

Talent Intelligence

Track hiring patterns across companies, markets, and job families. Build searchable datasets to identify demand trends by role, region, and employment type.

Recruitment Sourcing

Collect high-quality openings for outreach pipelines. Filter by relevant keywords and locations to prioritize matching opportunities faster.

Salary and Market Analysis

Monitor salary visibility, work arrangements, and role requirements across listings. Use historical runs to evaluate market movement over time.

Job Board Monitoring

Run scheduled collections to detect newly posted opportunities and deadlines. Power alerting workflows for teams that need timely updates.

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Input Parameters

Parameter	Type	Required	Default	Description
startUrl	String	No	"https://himalayas.app/jobs"	Start from one Himalayas jobs/search URL.
keyword	String	No	"software engineer"	Search keyword when URL inputs are not provided.
location	String	No	"United States"	Optional location filter for keyword-based runs.
collectDetails	Boolean	No	true	Include raw_job payload in output for advanced use.
results_wanted	Integer	No	20	Maximum number of jobs to save.
max_pages	Integer	No	5	Maximum pages to scan per seed URL.
proxyConfiguration	Object	No	Apify Proxy preset	Proxy settings for run reliability.

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Output Data

Each dataset item may contain the following fields (fields with no value are omitted):

Field	Type	Description
title	String	Job title.
excerpt	String	Short summary/excerpt from listing.
company	String	Hiring company name.
company_slug	String	Company slug from listing data.
company_logo	String	Company logo URL.
location	String	Consolidated location text.
applicant_location_requirements	Array	Explicit applicant location requirements.
timezone_restrictions	Array	Allowed timezone offsets for candidates.
categories	Array	Job categories/tags from source data.
parent_categories	Array	Parent category labels from source data.
date_posted	String	Job posting date (ISO).
apply_before	String	Application deadline when provided.
Description_html	String	Sanitized rich job description HTML (no script/style/href/src attributes).
Description_text	String	Plain-text version of description HTML.
salary	String	Human-readable salary summary.
salary_min	Number	Minimum salary value.
salary_max	Number	Maximum salary value.
salary_currency	String	Salary currency code.
job_type	String	Primary employment type.
employment_types	Array	Full list of employment types.
remote	Boolean	Whether role is marked remote.
seniority	Array	Seniority values from source data.
raw_job	Object	Full source job object (only when collectDetails is true).
url	String	Canonical job URL.
source	String	Data source label.
scraped_at	String	ISO timestamp of extraction.

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Usage Examples

Basic Keyword Search

Collect software engineering jobs with default detail collection:

{
  "keyword": "software engineer",
  "location": "United States",
  "results_wanted": 20
}

URL-Based Collection

Start from a specific jobs URL:

{
  "startUrl": "https://himalayas.app/jobs?q=data+engineer",
  "results_wanted": 50,
  "max_pages": 5,
  "collectDetails": true
}

Targeted Keyword Run

Use a focused keyword and location combination:

{
  "keyword": "python engineer",
  "location": "Canada",
  "results_wanted": 100,
  "max_pages": 8
}

Lightweight Fast Run

Skip extended details for faster scans:

{
  "keyword": "customer success",
  "location": "Remote",
  "collectDetails": false,
  "results_wanted": 30,
  "max_pages": 3
}

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Sample Output

{
  "title": "Senior Data Engineer",
  "excerpt": "Build scalable data systems for global analytics workloads.",
  "company": "Example Labs",
  "company_slug": "example-labs",
  "company_logo": "https://cdn-images.himalayas.app/example-logo",
  "location": "United States, Canada",
  "applicant_location_requirements": ["United States", "Canada"],
  "timezone_restrictions": [-8, -7, -6, -5],
  "categories": ["Data-Engineer", "Software-Engineer"],
  "parent_categories": ["Developer"],
  "date_posted": "2026-04-01T10:22:00.000Z",
  "apply_before": "2026-05-01T10:22:00.000Z",
  "Description_html": "<p>We are looking for a <strong>senior data engineer</strong> to build scalable data systems.</p><ul><li>Python</li><li>SQL</li></ul>",
  "Description_text": "We are looking for a senior data engineer to build scalable data systems. Python SQL",
  "salary": "120000-160000 USD",
  "salary_min": 120000,
  "salary_max": 160000,
  "salary_currency": "USD",
  "job_type": "Full Time",
  "employment_types": ["Full Time"],
  "remote": true,
  "seniority": ["Senior"],
  "url": "https://himalayas.app/companies/example-labs/jobs/senior-data-engineer",
  "source": "himalayas.app",
  "scraped_at": "2026-04-08T08:35:12.220Z"
}

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Tips for Best Results

Start Small, Then Scale

• Use results_wanted around 20-50 for quick validation.
• Increase limits only after confirming your filters produce relevant jobs.

Use Specific Search Intent

• Prefer focused queries like "backend engineer" over broad terms like "engineer".
• Combine keyword and location when you need tighter targeting.

Balance Depth and Speed

• Keep collectDetails set to true when you need enriched fields.
• Set collectDetails to false for faster monitoring runs.

Keep Data Clean

• Canonical URL deduplication is always applied internally to avoid repeated records.
• Run periodic exports to keep downstream datasets current.

Improve Stability on Large Runs

• Use proxy configuration when collecting many pages.
• Increase max_pages gradually to avoid unnecessary retries.

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Integrations

Connect your collected data with:

• Google Sheets — Build collaborative tracking sheets.
• Airtable — Create searchable recruiting databases.
• Slack — Send job alerts to hiring channels.
• Zapier — Trigger no-code automations.
• Make — Orchestrate multi-step workflows.
• Webhooks — Deliver records to your own systems.

Export Formats

• JSON — Structured integration and application use.
• CSV — Spreadsheet analysis and reporting.
• Excel — Business-friendly data sharing.
• XML — Legacy or enterprise integration flows.

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Frequently Asked Questions

Can I run with only a URL and no keyword?

Yes. You can provide startUrl without a keyword.

Can I collect jobs from multiple searches in one run?

Run the actor multiple times with different startUrl or keyword/location inputs, then combine datasets downstream.

Why are some fields missing from some jobs?

Different postings expose different metadata. The actor saves all available fields and omits empty ones.

How can I make runs faster?

Lower results_wanted and max_pages, or set collectDetails to false.

How can I avoid duplicate records?

Deduplication is automatic using canonical job URLs.

Is this suitable for scheduled monitoring?

Yes. It works well for periodic runs that track new postings and market movement.

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Support

For issues or enhancement requests, use the actor support channel in the Apify Console.

Resources

• Apify Documentation
• Apify API Reference
• Apify Scheduling

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Legal Notice

This actor is intended for legitimate data collection and analysis workflows. Users are responsible for ensuring compliance with applicable laws, website terms, and internal data-governance policies.