SimplyHired Jobs Scraper — Titles, Salary, Company & Skills

SimplyHired Jobs Scraper — Titles, Salary, Company, Skills & Full Descriptions

Extract comprehensive job postings from SimplyHired into clean, structured data without coding or manual browser intervention. This SimplyHired scraper crawls jobs by keyword and geographic location, capturing vital employment intelligence: job title, employer name, employer star rating, published salary ranges, employment classifications, required skills array, benefit packages, application links, geographic coordinates, and full job descriptions.

Export structured datasets to JSON, CSV, Excel, or consume records programmatically via the Apify REST API.

Built for recruiters, talent intelligence analysts, HR technology platforms, compensation researchers, job aggregators, and corporate sourcing teams who require automated, large-scale US job market data extraction.

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⚡ At a glance

Feature	Specification
Target Platform	SimplyHired (United States Nationwide Employment Exchange)
Data Types	Job Postings, Employer Profiles, Salary Disclosures, Skill Arrays
Search Filters	Multi-keyword list, US city/state/ZIP code, nationwide scope
Pagination Mode	Continuous cursor-based deep pagination (maxPagesPerSearch: 0 for auto/unlimited)
Throughput Speed	Fast listing mode: ~200–500 jobs / min; Detail mode: ~60–120 jobs / min
Data Enrichment	Structured salary min/max, full sanitized description, skills, company rating
Output Formats	JSON, CSV, XML, Excel (XLSX), HTML Table
Default Concurrency	5 concurrent detail enrichment workers (configurable up to 10)
Proxy Compatibility	Datacenter first with automatic US Residential fallback

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🔎 Why scrape SimplyHired?

SimplyHired functions as one of North America's premier employment aggregators, consolidating millions of active openings directly from corporate career sites, direct employers, staffing agencies, and partner job boards. Each listing encapsulates valuable market signals: who is actively hiring, published wage distributions, required qualifications, and workplace arrangements (remote, hybrid, or on-site).

Extracting this data manually is hindered by 20-item pagination limits, unformatted compensation strings, and nested descriptions requiring individual page clicks. This SimplyHired data extractor automates the end-to-end workflow: sequentially resolving cursor tokens, harvesting multi-page search results, deduplicating records across queries, and returning standardized datasets ready for ATS pipelines and analytics databases.

Core enterprise use cases

• Compensation & Salary Benchmarking: Aggregate salaryMin and salaryMax distributions segmented by metropolitan area and job title to establish competitive wage benchmarks.
• Recruitment & Sourcing Pipelines: Stream fresh job opportunities and employer expansion signals into applicant tracking systems (ATS) and talent CRM databases.
• Skill Demand & Tech Trend Analysis: Track rising technical skills (e.g., Python, Kubernetes, Salesforce) and qualification prerequisites across specific industries.
• Competitive Hiring Intelligence: Observe competitor workforce expansion and regional talent acquisition velocity in real time.
• Aggregated Job Feed Generation: Seed specialized niche job boards and regional candidate portals with structured, deduplicated vacancy feeds.

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✨ Core capabilities

• ✅ Multi-Keyword Search Execution: Crawl multiple job titles or roles simultaneously ("software engineer", "registered nurse", "accountant").
• ✅ Geographic Precision: Filter by US metropolitan area, state code, 5-digit ZIP, or leave empty for nationwide aggregation.
• ✅ Cursor-Based Deep Pagination: Unlocked pagination engine traverses consecutive pagination tokens without artificial item caps.
• ✅ Comprehensive Listing Metadata: Captures job titles, employer names, company ratings, verbatim compensation text, job types, and remote flags directly from search pages.
• ✅ Full Detail Page Enrichment: Optionally inspect dedicated job postings to capture full HTML-stripped descriptions, normalized canonical titles, structured salary objects, and application URLs.
• ✅ Structured Salary Extraction: Parses compensation ranges into numeric salaryMin, salaryMax, and salaryPeriod properties (YEAR, HOUR, MONTH).
• ✅ Native Deduplication: Ensures every vacancy is uniquely validated by SimplyHired jobKey, eliminating duplicate entries across overlapping search queries.
• ✅ Enterprise Export Standards: Export clean data directly to CSV, JSON, Excel, or consume programmatically via webhooks.

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📥 Input parameters

The scraper accommodates both high-speed search card discovery and deep detail page extraction. All fields are optional; running without input initiates a default software engineering exploration.

Parameter	Type	Default	Description
searchQueries	Array of Strings	["software engineer"]	Job titles, role keywords, or skill names to search.
location	String	"New York, NY"	Target city, state, or ZIP code (leave blank for nationwide scope).
includeDetails	Boolean	false	When enabled, opens individual job pages to extract full descriptions, coordinates, and apply URLs.
maxPagesPerSearch	Integer	0	Maximum result pages per keyword (20 jobs/page). Set to 0 for unlimited pagination.
maxItems	Integer	50	Maximum total job postings to retain across all keywords. Set to 0 for unlimited.
maxConcurrency	Integer	5	Concurrency for detail page enrichment (1–10).
proxyConfiguration	Object	{ "useApifyProxy": true }	Proxy configuration (datacenter with automatic US residential fallback).

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📋 Ready-to-use input examples

Example 1: High-speed listing discovery in Austin

{
  "searchQueries": ["Data Analyst", "Data Engineer"],
  "location": "Austin, TX",
  "includeDetails": false,
  "maxItems": 100,
  "maxPagesPerSearch": 0
}

Example 2: Full-detail remote healthcare roles

{
  "searchQueries": ["Telehealth Nurse", "Case Manager"],
  "location": "Remote",
  "includeDetails": true,
  "maxItems": 150,
  "maxConcurrency": 5
}

Example 3: Nationwide engineering sweep

{
  "searchQueries": ["DevOps Engineer"],
  "location": "",
  "includeDetails": false,
  "maxItems": 500,
  "maxPagesPerSearch": 0
}

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📤 Output dataset structure

Every vacancy produces a clean JSON record. Below is a representative output record with includeDetails: true:

{
  "jobKey": "t4bWK0x5rDdzE6qEiBJ_67Y80jGO0E9j",
  "title": "Senior Backend Software Engineer",
  "normalizedTitle": "senior backend software engineer",
  "company": "Steadily Insurance Agency",
  "companyRating": 4.1,
  "location": "Austin, TX",
  "url": "https://www.simplyhired.com/job/t4bWK0x5rDdzE6qEiBJ_67Y80jGO0E9j",
  "salaryText": "$150,000 - $185,000 a year",
  "salaryMin": 150000,
  "salaryMax": 185000,
  "salaryPeriod": "YEAR",
  "salaryCurrency": "USD",
  "jobTypes": [
    "Full-time"
  ],
  "remoteAttributes": [],
  "workSettings": [
    "Hybrid"
  ],
  "benefits": [
    "Health insurance",
    "401(k) matching",
    "Dental insurance",
    "Paid time off"
  ],
  "requirements": [
    "Mid-level",
    "Python",
    "4 years experience"
  ],
  "skills": [
    "Python",
    "PostgreSQL",
    "Docker",
    "FastAPI",
    "AWS"
  ],
  "qualifications": [
    "Python",
    "Distributed systems",
    "Production APIs"
  ],
  "snippet": "We are seeking a Senior Backend Engineer to architect cloud microservices and scale core insurance platforms...",
  "city": "Austin",
  "state": "TX",
  "latitude": 30.2715,
  "longitude": -97.7419,
  "description": "Full job description text cleanly normalized into readable paragraphs without extraneous HTML tags...",
  "applyUrl": "https://www.simplyhired.com/out?r=...",
  "companyLogo": "https://d3qscgr6xsioh.cloudfront.net/logos/steadily.png",
  "companyPageUrl": "https://www.simplyhired.com/company/steadily-insurance",
  "datePosted": "2026-11-04T08:30:00.000Z",
  "sponsored": false,
  "easyApply": true,
  "expired": false,
  "searchQuery": "backend engineer",
  "searchLocation": "Austin, TX",
  "detailScraped": true,
  "scrapedAt": "2026-11-10T14:45:00.000Z"
}

---

📊 Field definitions & coverage

💼 Core job metadata

Field	Type	Description	Coverage
jobKey	String	Unique SimplyHired job identifier (deduplication key)	🟢 High (100%)
title	String	Original job title as published	🟢 High (100%)
normalizedTitle	String	Standardized job taxonomy title (details on)	🟢 High (98%)
url	String	Canonical URL to the SimplyHired job posting	🟢 High (100%)
snippet	String	Short excerpt displayed on search cards	🟢 High (100%)
description	String	Complete plain text sanitized job specification (details on)	🟢 High (99%)
datePosted	String	ISO 8601 publication timestamp	🟢 High (100%)
easyApply	Boolean	True if SimplyHired one-click application is supported	🟢 High (100%)
sponsored	Boolean	Flag indicating sponsored employer placement	🟢 High (100%)
applyUrl	String	Direct external application URL (details on)	🟢 High (92%)

🏢 Employer details

Field	Type	Description	Coverage
company	String	Legal or operating employer name	🟢 High (100%)
companyRating	Number	Employer review star rating (1.0–5.0 scale)	🟡 Conditional (70%)
companyLogo	String	CDN URL to the corporate logo image (details on)	🟡 Conditional (75%)
companyPageUrl	String	SimplyHired corporate overview profile link (details on)	🟡 Conditional (80%)

📍 Location & workplace arrangement

Field	Type	Description	Coverage
location	String	Formatted display location string	🟢 High (100%)
city	String	Municipality jurisdiction (details on)	🟢 High (94%)
state	String	Two-letter state postal abbreviation (details on)	🟢 High (94%)
latitude / longitude	Number	Geocoded geographic coordinates (details on)	🟢 High (90%)
workSettings	Array	Workplace tags (Remote, Hybrid, In-Office)	🟢 High (90%)

💰 Compensation & qualifications

Field	Type	Description	Coverage
salaryText	String	Verbatim compensation string as published	🟡 Conditional (75%)
salaryMin	Number	Parsed minimum compensation numeric value (details on)	🟡 Conditional (55%)
salaryMax	Number	Parsed maximum compensation numeric value (details on)	🟡 Conditional (55%)
salaryPeriod	String	Pay period frequency (YEAR, HOUR, MONTH) (details on)	🟡 Conditional (55%)
salaryCurrency	String	Currency standard (USD, CAD) (details on)	🟡 Conditional (55%)
skills	Array	Required or preferred technology and professional skills	🟢 High (95%)
benefits	Array	Disclosed employee benefits (healthcare, 401k, PTO)	🟢 High (85%)
qualifications	Array	Explicit prerequisites and experience thresholds	🟢 High (80%)

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💡 Search strategies & best practices

1. High-Speed Aggregation First: When collecting broad talent market data, run with includeDetails: false. The listing view provides salary text, company ratings, and skill tags at high throughput (~500 records/minute).
2. Selective Detail Enrichment: Identify high-priority job openings from the listing run, then re-execute specifically for those jobKey values with includeDetails: true to capture full descriptions and apply URLs.
3. Multi-Query Keyword Sweeps: Group related titles together (e.g. ["Nurse Practitioner", "Family Nurse Practitioner", "ARNP"]) to capture total market inventory. The actor automatically discards duplicate postings.
4. Scheduled Automated Ingestion: Set up an Apify Schedule (e.g. daily at 07:00 AM) to capture fresh vacancies published over the last 24 hours, feeding new records directly into your sourcing CRM.

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⚙️ How to use

1. Enter target job keywords in searchQueries (e.g. ["Product Manager"]) and define a location.
2. Choose whether to enable includeDetails based on your project requirements.
3. Set maxItems and keep maxPagesPerSearch: 0 for complete cursor-based pagination.
4. Click Start in the Apify Console and monitor live event logs.
5. Export your structured job dataset in JSON, CSV, or Excel formats.

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💻 API & Integration examples

cURL

curl -X POST "https://api.apify.com/v2/acts/haketa~simplyhired-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQueries": ["Data Scientist"],
    "location": "Boston, MA",
    "includeDetails": false,
    "maxItems": 100,
    "maxPagesPerSearch": 0
  }'

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "searchQueries": ["DevOps Engineer"],
    "location": "Seattle, WA",
    "includeDetails": True,
    "maxItems": 150
}

run = client.actor("haketa/simplyhired-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item.get('title')} at {item.get('company')} — Salary: {item.get('salaryText', 'Not Listed')}")

Node.js / JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
    token: 'YOUR_APIFY_TOKEN',
});

const runInput = {
    searchQueries: ['Software Engineer'],
    location: 'New York, NY',
    maxItems: 50,
    includeDetails: false,
};

const run = await client.actor('haketa/simplyhired-scraper').call(runInput);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log(`Extracted ${items.length} SimplyHired job records.`);

Webhook & Workflow Automation

Connect this scraper directly to your workflow tools:

• Make (Integromat): Create a scenario triggered by an Apify "Run Succeeded" webhook. Parse the resulting dataset and automatically append records to Google Sheets, Notion, or Airtable.
• Zapier: Connect the Apify app, listen for dataset item creation, and send Slack notifications whenever a target company posts a new executive technology role.
• AWS S3 / Google Cloud Storage: Use Apify Integrations to automatically sync every completed dataset to cloud storage buckets for enterprise data lakes.

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❓ Frequently Asked Questions

How does deep pagination function on SimplyHired? SimplyHired delivers listings in ~20-job pages navigated via sequential cursor tokens. When maxPagesPerSearch is set to 0, the scraper automatically traverses every available cursor until maxItems is satisfied or all matching positions are retrieved.

Why are structured salary fields null on some records? Compensation disclosures on SimplyHired depend on employer transparency. When an employer publishes an explicit salary or hourly wage, the scraper captures the text and parses it into salaryMin and salaryMax. Postings without salary remain null for structured fields.

What proxy settings are recommended? The scraper features automated proxy routing that utilizes datacenter IPs first, automatically rotating to US residential proxies when necessary. Retaining the default { "useApifyProxy": true } is strongly recommended.

Can I run scheduled sweeps for fresh postings? Yes. You can configure an Apify Schedule (e.g., daily at 08:00 UTC) to build an automated pipeline capturing new vacancies every morning without duplicates.

What is the difference between listing summary and full detail mode? In listing summary mode (includeDetails: false), the scraper extracts metadata visible on search result cards (title, company, display location, salary text, skills snippet, posted date) at very high speeds (~500 items/minute). In full detail mode (includeDetails: true), the actor concurrently visits each posting's dedicated view to extract full HTML-stripped descriptions, normalized job titles, company profile links, and direct application URLs.

Can I export data directly to Excel or CSV? Yes. From the Apify Dataset tab, you can download all extracted fields in CSV, Excel (XLSX), JSON, XML, or HTML table formats with a single click.

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🔗 Related employment scrapers

• Dice Job Scraper — Deep technology recruitment intelligence and developer skill tags.
• ZipRecruiter Jobs Scraper — Multi-country job board data with parsed compensation.
• CutShort Jobs Scraper — India tech job listings and developer salary data.
• Glassdoor Jobs Scraper — Company reviews, salaries, and open positions.

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📋 Changelog

v1.2 (2026-11)

• Deep Cursor Pagination Unlock: Unlocked 1,000+ continuous result pagination with configurable maxPagesPerSearch (0 = auto/unlimited) and removal of legacy page bounds.
• Fast Prefill Optimization: Streamlined default execution parameters ensuring sample test runs complete in under 30 seconds.
• Schema Hardening: Standardized integer limits to minimum 0 and cleansed dataset schemas for strict specification compliance.
• Deduplication Safeguards: Hardened jobKey verification preventing duplicate records across overlapping multi-keyword search queries.

v1.1 (2026-10)

• Enhanced structured salary parser extracting annual and hourly compensation objects.
• Added support for normalized canonical job titles in detail enrichment mode.

v1.0 (2026-09)

• Initial production release supporting multi-keyword discovery, location filtering, and structured JSON/CSV exports.