Dice.com Job Scraper — Tech Jobs, Salary, Skills & Companies

Scrape Dice.com technology and IT job postings into clean, structured datasets — without account credentials, browser overhead, or manual data entry. This Dice scraper executes deep pagination over keywords and locations, filters by posted date or workplace model, or accepts direct job detail URLs, delivering comprehensive tech market intelligence: job title, hiring company, structured location, remote/on‑site classification, employment type, structured salary range, required skills array, posted date, application deadline, and sanitized full job descriptions.

Export structured data to JSON, CSV, Excel, or consume directly via the Apify REST API.

Built for tech recruiters, staffing agencies, compensation analysts, talent intelligence researchers, job market aggregators, and enterprise engineering teams requiring reliable, scalable Dice job data extraction without limits.

---

⚡ At a glance

Feature	Specification
Target Platform	Dice.com (United States Tech & IT Careers)
Data Types	Tech Job Postings, Employer Profiles, Salary Benchmarks, Skill Tags
Search Options	Multi-keyword list, US city/state/remote location, job URLs
Pagination Mode	Automated deep pagination (unlimited or bounded per keyword)
Throughput Speed	~150–500 jobs / minute with parallel detail enrichment
Data Enrichment	Skills array, min/max salary parsing, deadline, HTML-stripped description
Output Formats	JSON, CSV, XML, Excel (XLSX), HTML Table, RSS
Default Concurrency	8 concurrent workers (configurable up to 12)
Proxy Compatibility	Apify Proxy (Datacenter or Residential, US IPs recommended)

---

🔎 Why scrape Dice.com?

Dice.com stands as the definitive recruitment exchange for technology, software development, cloud infrastructure, cybersecurity, and enterprise IT roles across North America. Postings on Dice carry dense, high-signal information: exactly which organizations are scaling, the technology frameworks required, compensation floors and ceilings, and the evolving distribution of remote versus hybrid arrangements.

Manually inspecting Dice job postings is labor-intensive. Search result previews redact complete skill profiles, compensation nuances, application deadlines, and full role specifications behind individual job detail views. This Dice.com scraper streamlines extraction into an automated workflow: paginating deep across multiple queries, harvesting metadata, opening job details in parallel, and returning a normalized, deduplicated dataset.

Common enterprise use cases

• B2B Tech Lead Generation: Identify companies actively investing in specific software stacks (e.g., Snowflake, Kubernetes, Salesforce, Golang) to target recruitment services or B2B SaaS software.
• Compensation & Salary Benchmarking: Analyze granular salaryMin and salaryMax distributions segmented by geography, experience level, and tech stack to construct competitive compensation packages.
• Skill Demand & Market Trends: Track shifting technical requirements across time (e.g., LLM engineering, Rust, AWS migration) to inform workforce planning and curriculum design.
• Automated ATS & Job Board Ingestion: Programmatically sync current tech openings into specialized job discovery platforms, regional job feeds, and talent matching pipelines.
• Competitive Intelligence: Monitor rival tech teams' expansion velocity, organizational structures, and strategic engineering bets through active vacancy tracking.

---

✨ Core capabilities

• ✅ Multi-Keyword Search: Crawl multiple roles, skill terms, or company names simultaneously (DevOps Engineer, Data Scientist, Kubernetes). Each query executes independent pagination.
• ✅ Geographic & Workplace Filtering: Target nationwide positions, specific US metropolitan areas, or filter strictly by Remote, Hybrid, or On-Site environments.
• ✅ Recency Window Selection: Focus on freshly published opportunities with recency bounds (Past 24 hours, Past 3 days, Past 7 days, or Any).
• ✅ Direct URL Ingestion: Provide an array of specific Dice job URLs or GUIDs for targeted re-scraping and attribute verification.
• ✅ Deep 1,000+ Pagination: Unlocked pagination engine pages cleanly past 30-item listing thresholds, retrieving thousands of matching roles without artificial caps.
• ✅ Dual-Source Salary Extraction: Captures machine-readable compensation schemas and parses human-readable salary text into numerical salaryMin and salaryMax ranges.
• ✅ Standardized Tech Skills Array: Extracts specific developer skills and technology tags into structured arrays ready for filtering and indexing.
• ✅ Clean Plaintext Descriptions: Converts complex HTML markup into readable, whitespace-normalized plaintext descriptions without broken tags.
• ✅ Native Deduplication: Ensures every job record is uniquely validated by Dice guid, eliminating redundant rows across overlapping search queries.

---

🎯 Primary workflows

• Recruitment & Executive Search: Surface passive engineering openings, uncover hiring managers, and identify contract/C2C opportunities for specialized consultants.
• HR Tech & Labor Analytics: Conduct econometric studies on technical compensation, geographical wage premiums, and remote work adoption rates.
• SaaS Sales Prospecting: Discover high-growth companies that recently posted 20+ engineering roles to prioritize outbound enterprise sales motions.
• Freelance & Contract Aggregation: Filter specifically for contract and corp-to-corp designations to feed consultant placement workflows.

---

📥 Input parameters

The scraper accepts flexible inputs ranging from broad keyword discoveries to targeted direct URLs. All fields are optional; running without input initiates a default software engineering exploration.

Parameter	Type	Default	Description
searchQueries	Array of Strings	["python developer"]	Target job keywords, roles, technologies, or company names.
location	String	""	Optional location filter (US city, state code, or Remote). Leave empty for nationwide scope.
postedDate	Select	"Any"	Recency filter: Any, Past 24 hours, Past 3 days, or Past 7 days.
workplaceType	Select	"Any"	Workplace classification: Any, Remote, Hybrid, or On-Site.
jobUrls	Array of Strings	[]	Explicit Dice job URLs or GUIDs to scrape directly.
includeJobDetails	Boolean	true	When enabled, opens detail pages to capture full descriptions, skill arrays, structured pay, and deadlines.
maxPages	Integer	0	Maximum search result pages to fetch per query (30 jobs/page). Set to 0 for unlimited pagination.
maxItems	Integer	100	Total maximum job postings to retain across all keywords and direct URLs.
maxConcurrency	Integer	8	Parallel detail request concurrency limit (recommended 6–10).
proxyConfiguration	Object	{ "useApifyProxy": true }	Apify Proxy settings. US IP routing provides maximum coverage.

---

📋 Ready-to-use input examples

Example 1: Full-detail remote software roles

{
  "searchQueries": ["Full Stack Engineer", "Backend Developer"],
  "workplaceType": "Remote",
  "postedDate": "Past 7 days",
  "includeJobDetails": true,
  "maxItems": 150,
  "maxPages": 0
}

Example 2: High-speed enterprise discovery (listing summary)

{
  "searchQueries": ["Data Engineer", "Machine Learning", "Cloud Architect"],
  "location": "New York",
  "includeJobDetails": false,
  "maxItems": 500,
  "maxPages": 0
}

Example 3: Targeted re-scraping via direct URLs

{
  "jobUrls": [
    "https://www.dice.com/job-detail/37698878-e6a8-4218-97b3-5901bdce9e1c",
    "https://www.dice.com/job-detail/209494b1-644d-416f-b9e6-d0c5c1b70bbb"
  ],
  "includeJobDetails": true
}

---

📤 Output dataset structure

Every harvested vacancy produces a comprehensive JSON record. Below is a representative output record:

{
  "guid": "37698878-e6a8-4218-97b3-5901bdce9e1c",
  "title": "Senior Cloud Infrastructure Engineer",
  "companyName": "Apex Systems",
  "companyProfileId": "8c2cf2a7-dcd7-42dc-b8d4-2b90acef1688",
  "location": "Merrimack, New Hampshire, USA",
  "city": "Merrimack",
  "state": "NH",
  "postalCode": "03054",
  "country": "US",
  "isRemote": false,
  "workplaceTypes": [
    "On-Site"
  ],
  "employmentType": "Full-time",
  "salary": "USD 130,000.00 - 155,000.00 per year",
  "salaryMin": 130000,
  "salaryMax": 155000,
  "salaryCurrency": "USD",
  "skills": [
    "AWS",
    "Terraform",
    "Kubernetes",
    "Docker",
    "Python",
    "CI/CD",
    "Linux"
  ],
  "postedDate": "2026-11-01T08:15:00Z",
  "modifiedDate": "2026-11-02T10:30:00Z",
  "datePosted": "2026-11-01T08:15:00.000Z",
  "validThrough": "2026-12-15T23:59:59.000Z",
  "easyApply": true,
  "applyType": "Internal",
  "employerType": "Recruiter",
  "summary": "Seeking a Senior Cloud Infrastructure Engineer to lead automated deployment architectures and multi-region AWS operations...",
  "description": "Full job description text formatted cleanly into readable paragraphs without extraneous HTML tags...",
  "companyLogoUrl": "https://d3qscgr6xsioh.cloudfront.net/logos/apex_systems.png",
  "companyProfileUrl": "https://www.dice.com/company-profile/8c2cf2a7-dcd7-42dc-b8d4-2b90acef1688",
  "url": "https://www.dice.com/job-detail/37698878-e6a8-4218-97b3-5901bdce9e1c",
  "detailEnriched": true,
  "sourceMode": "search",
  "scrapedAt": "2026-11-10T14:20:00.000Z"
}

---

📊 Field definitions & coverage

💼 Core job metadata

Field	Type	Description	Coverage
guid	String	Unique Dice job identifier and deduplication key	🟢 High (100%)
title	String	Formal job title	🟢 High (100%)
url	String	Canonical URL to the Dice job posting	🟢 High (100%)
summary	String	Short excerpt shown on search result listings	🟢 High (100%)
description	String	Full sanitized job description plain text	🟢 High (99%)
skills	Array	Structured technology skill tags	🟢 High (92%)
easyApply	Boolean	Flag indicating if Dice 1-click application is supported	🟢 High (100%)
applyType	String	Application routing mechanism (Internal or External)	🟢 High (98%)

🏢 Company & employer attributes

Field	Type	Description	Coverage
companyName	String	Legal or operating employer name	🟢 High (100%)
companyProfileId	String	Unique Dice internal employer identifier	🟢 High (95%)
companyProfileUrl	String	Link to corporate Dice employer showcase	🟢 High (95%)
companyLogoUrl	String	CDN URL to the corporate logo graphic	🟡 Conditional (80%)
employerType	String	Hiring classification (Direct Hire, Recruiter, etc.)	🟢 High (90%)

📍 Location & workplace arrangement

Field	Type	Description	Coverage
location	String	Formatted display location string	🟢 High (100%)
city	String	Municipal jurisdiction	🟢 High (92%)
state	String	State or provincial code	🟢 High (92%)
postalCode	String	ZIP or postal code identifier	🟡 Conditional (75%)
country	String	ISO country code	🟢 High (95%)
isRemote	Boolean	True if posting permits remote arrangement	🟢 High (100%)
workplaceTypes	Array	Work model classifications (Remote, Hybrid, On-Site)	🟢 High (100%)

💰 Compensation & engagement terms

Field	Type	Description	Coverage
employmentType	String	Engagement category (Full-time, Contract, Third Party)	🟢 High (100%)
salary	String	Original verbatim salary text as published	🟡 Conditional (70%)
salaryMin	Number	Normalized minimum compensation value	🟡 Conditional (65%)
salaryMax	Number	Normalized maximum compensation value	🟡 Conditional (65%)
salaryCurrency	String	Standard currency denomination (USD, CAD, etc.)	🟡 Conditional (65%)

⏱️ Timeline & tracking metadata

Field	Type	Description	Coverage
postedDate	String	ISO timestamp of original publication	🟢 High (100%)
modifiedDate	String	ISO timestamp of most recent listing update	🟢 High (98%)
validThrough	String	Employer-specified application deadline	🟡 Conditional (85%)
detailEnriched	Boolean	True if the posting was parsed via deep detail page inspection	🟢 High (100%)
sourceMode	String	Discovery origin (search or direct)	🟢 High (100%)
scrapedAt	String	ISO timestamp of scraper extraction	🟢 High (100%)

---

💡 Search strategies & best practices

To maximize the yield and precision of your Dice data collection, consider applying these proven search strategies:

1. Target Specific Engineering Stacks: Instead of broad queries like "Developer", query role + core technology combinations such as "Senior React Node Developer", "Kubernetes Cloud Architect", or "Golang Backend Engineer". Specific queries yield higher relevance and clearer salary benchmarks.
2. Multi-Query Geographic Sweeps: Provide a list of complementary keywords (e.g., ["Data Engineer", "Analytics Engineer", "BI Developer"]) alongside a targeted metropolitan area ("Austin", "Seattle", "New York") to harvest an entire regional talent market in a single execution.
3. Daily Delta Monitoring: Establish an Apify Scheduled Actor task running daily at 06:00 AM with postedDate: "Past 24 hours". Pipe the new records directly to your recruiting database or ATS via webhooks for immediate outreach to newly posted positions.
4. Two-Stage Extraction Pipeline: When monitoring massive volumes (5,000+ positions), run a fast discovery crawl with includeJobDetails: false. Analyze high-level trends across title, company, and location. Then feed the harvested guid or url values back into the scraper with includeJobDetails: true only for high-priority targets.
5. Deduplication Across Overlapping Roles: When searching similar technology keywords, companies often cross-post the same opening under different tags. The actor's internal deduplication engine automatically discards redundant entries based on the canonical guid, guaranteeing zero duplicated records in your final dataset.

---

⚙️ How to use

1. Configure Search Parameters: Enter your target keywords into searchQueries (e.g., ["React Developer", "Node.js"]), and specify an optional location.
2. Select Enrichment Depth: Keep includeJobDetails: true to collect full descriptions and skills, or set to false for high-velocity title/company directory pulls.
3. Set Extraction Limits: Define maxItems based on your project volume requirements. Leave maxPages: 0 for automatic pagination across all pages.
4. Execute Run: Click Start in the Apify Console. Monitor live event logs as records stream directly into the Apify Dataset.
5. Download or Integrate: Export datasets in JSON, CSV, or Excel formats, or consume items programmatically via webhook notifications and REST endpoints.

---

💻 API & Integration examples

cURL

curl -X POST "https://api.apify.com/v2/acts/haketa~dice-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQueries": ["Python Developer"],
    "location": "San Francisco",
    "workplaceType": "Remote",
    "maxItems": 100,
    "includeJobDetails": true
  }'

Python

from apify_client import ApifyClient

# Initialize client with your personal token
client = ApifyClient("YOUR_APIFY_TOKEN")

# Prepare payload
run_input = {
    "searchQueries": ["Data Engineer"],
    "location": "Remote",
    "postedDate": "Past 7 days",
    "maxItems": 250,
    "includeJobDetails": True
}

# Run the actor and wait for completion
run = client.actor("haketa/dice-scraper").call(run_input=run_input)

# Fetch dataset items
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item.get('title')} at {item.get('companyName')} - {item.get('salary', 'N/A')}")

Node.js / JavaScript

import { ApifyClient } from 'apify-client';

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

const runInput = {
    searchQueries: ['DevOps Engineer'],
    workplaceType: 'Remote',
    maxItems: 100,
    includeJobDetails: true,
};

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

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

Webhook & Zapier 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 or Airtable.
• Zapier: Connect the Apify app, listen for dataset item creation, and send Slack alerts 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.

---

❓ Frequently Asked Questions

How does deep pagination function on Dice? Dice delivers search results in 30-item pages. When maxPages is set to 0, the scraper automatically advances sequentially through all available pages until maxItems is satisfied or the total available result count reported by Dice is exhausted.

Why are some salary values null? Compensation disclosures on Dice are contingent upon employer publication. Many employers choose not to publish salary amounts or mark them as "Competitive" or "Depends on Experience". When a numeric salary or range is provided anywhere in the posting, the scraper extracts and parses it into salaryMin and salaryMax.

What is the difference between search summary and full detail mode? In search summary mode (includeJobDetails: false), the scraper extracts metadata visible on search result cards (title, company, display location, snippet, posted date) at very high speeds (~1,000 items in seconds). In full detail mode (includeJobDetails: true), the actor concurrently visits each posting's dedicated view to extract full HTML-stripped descriptions, technology skill tags, application deadlines, and structured compensation schemas.

Can I run scheduled sweeps for fresh postings? Yes. You can configure an Apify Schedule (e.g., daily at 08:00 UTC) with postedDate: "Past 24 hours" to build an automated pipeline capturing new vacancies every morning without duplicates.

Can I scrape by specific Dice job URLs or GUIDs? Yes. If you have an existing list of vacancy links (e.g. from an internal database or previous crawls), populate the jobUrls input array. The scraper will bypass keyword search and directly harvest the detailed records for those exact positions.

How does deduplication work across multiple search queries? If multiple keywords return the same posting (e.g., a role tagged under both "Kubernetes" and "DevOps"), the actor tracks previously saved positions by their unique Dice guid. Redundant entries are skipped instantly, ensuring zero duplicate rows in your dataset.

What proxy settings are recommended? Dice primarily services the North American market. Leaving the default { "useApifyProxy": true } configuration automatically utilizes Apify Proxy with US IP addresses for optimal routing, low latency, and maximum data completeness.

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.

---

🔗 Complementary scrapers

• SimplyHired Jobs Scraper — Extract nationwide US job opportunities and employer ratings.
• CutShort Jobs Scraper — Source tech talent and developer compensation across Indian tech hubs.
• ZipRecruiter Jobs Scraper — Scrape multi-country job boards with parsed compensation data.
• Glassdoor Jobs Scraper — Comprehensive company reviews, salaries, and open positions.

---

📋 Changelog

v1.2 (2026-11)

• Deep Pagination Engine: Fully unlocked 1,000+ continuous result pagination with configurable maxPages (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.
• Dual-Source Salary Parsing: Enhanced regular expression parsing capturing salary ranges from both structured schema tags and natural-language text.
• Deduplication Safeguards: Hardened GUID verification preventing duplicate items across overlapping multi-keyword search queries.
• Input Schema Standardization: Updated limit boundaries to support minimum 0 values and zero-parameter empty runs with graceful software engineering fallback.

v1.1 (2026-10)

• Improved plaintext description cleaning with unescaped newline normalization.
• Enhanced skill array extraction matching both standard and custom tags.

v1.0 (2026-09)

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