Indeed Job Scraper

Extract structured job postings and hiring-company insights from Indeed search results at scale. This tool turns public listings into clean, analytics-ready data for research, recruiting, and market intelligence.

Use the Indeed job scraper to capture titles, companies, locations, salaries, posting dates, job types, and rich company metadata with robust filtering and high reliability.

     

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Introduction

This project programmatically collects job listings from Indeed search result pages and normalizes them into machine-readable JSON/CSV. It solves the pain of copy-pasting or dealing with noisy HTML by providing a stable, structured pipeline that’s ready for analytics, dashboards, or CRM enrichment.

Who is it for? Talent acquisition teams, market researchers, data engineers, sales/BD teams doing lead generation, and analysts benchmarking roles and salaries across regions.

Why structured job data matters

• Consistent, enriched fields (salary, reviews, remote type, job types) enable apples-to-apples analysis across searches.
• Filters and batching support large pulls for longitudinal trend tracking.
• Output is ready for BI tools, data warehouses, or downstream NLP (skills extraction, benefits, contacts).
• Designed for resilience against layout changes with strict validation and fallbacks.
• Supports integration into Python pipelines and no-code automation systems.

Features

Feature	Description
Search URL ingestion	Paste any Indeed search URL (with filters applied) to target precise roles, companies, and locations.
Rich field coverage	Captures titles, companies, salary snippets, ratings, reviews, locations, dates, job types, remote modes, and more.
Pagination & batching	Crawls multi-page results reliably with configurable limits and deduplication by job key.
Robustness & accuracy	Defensive parsing, schema validation, and fallbacks for dynamic UI changes.
Flexible export	Download data as JSON, CSV, or Excel; stream into pipelines or databases.
Cost-aware operation	Efficient traversal and throttling keep usage costs low at scale.
Compliance friendly	Focuses on publicly available job data with practical guardrails and documentation.

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What Data This Scraper Extracts

Field Name	Field Description
company	Company name as shown on the listing.
companyBrandingAttributes.headerImageUrl	Header image URL if present on the company card.
companyBrandingAttributes.logoUrl	Company logo URL if present.
companyOverviewLink	Link to the company’s profile/overview page.
companyRating	Average rating score displayed for the company.
companyReviewCount	Count of reviews associated with the company.
displayTitle	Display title of the job listing.
expired	Boolean indicating whether the job listing has expired.
extractedSalary.min	Parsed minimum salary value (numeric if available).
extractedSalary.max	Parsed maximum salary value (numeric if available).
extractedSalary.type	Compensation cadence (e.g., yearly, monthly) when detectable.
formattedLocation	Human-readable location string.
formattedRelativeTime	Relative posting time (e.g., “3 days ago”).
jobLocationCity	Parsed city component of the location (when available).
jobLocationState	Parsed state/region component (when available).
jobTypes	Array of job type tags (e.g., Full-time, Contract).
jobkey	Unique job identifier extracted from the listing.
link	Canonical link to the job details page.
locationCount	Count of locations associated with the posting (multi-location roles).
newJob	Boolean set when the listing is flagged as “new”.
normTitle	Normalized title for aggregation (e.g., “Software Engineer”).
pubDate	Publication date/time (ISO when available).
remoteWorkModel.type	Remote classification (REMOTE, HYBRID, ONSITE when detectable).
salarySnippet.text	Salary text snippet as displayed.
salarySnippet.currency	Currency code if parsed from snippet.
snippet	Short job description/summary.
sponsored	Boolean indicating a sponsored listing.
taxoAttributes / taxonomyAttributes	Structured attribute tags used by the platform (labels and tiers).
title	Title variant used in the job card.
urgentlyHiring	Boolean indicating an urgent hiring badge.
viewJobLink	Alternate link to the job view page (if present).

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Example Output

[
  {
    "company": "Acme Corp",
    "companyBrandingAttributes": {
      "headerImageUrl": "https://images.example.com/acme/header.jpg",
      "logoUrl": "https://images.example.com/acme/logo.png"
    },
    "companyOverviewLink": "https://www.indeed.com/cmp/Acme-Corp",
    "companyRating": 4.1,
    "companyReviewCount": 532,
    "displayTitle": "Senior Data Engineer",
    "expired": false,
    "extractedSalary": {
      "min": 140000,
      "max": 170000,
      "type": "yearly"
    },
    "formattedLocation": "New York, NY",
    "formattedRelativeTime": "3 days ago",
    "jobLocationCity": "New York",
    "jobLocationState": "NY",
    "jobTypes": ["Full-time"],
    "jobkey": "123abc456def",
    "link": "https://www.indeed.com/viewjob?jk=123abc456def",
    "locationCount": 1,
    "newJob": true,
    "normTitle": "Data Engineer",
    "pubDate": "2025-10-28T09:00:00Z",
    "remoteWorkModel": { "type": "REMOTE_HYBRID" },
    "salarySnippet": {
      "currency": "USD",
      "text": "$140,000 - $170,000 a year",
      "salaryTextFormatted": true,
      "source": "employer"
    },
    "snippet": "Design and optimize data pipelines in cloud environments...",
    "sponsored": false,
    "taxonomyAttributes": [
      { "label": "Python", "tier": "skill" },
      { "label": "ETL", "tier": "skill" }
    ],
    "title": "Senior Data Engineer",
    "urgentlyHiring": false,
    "viewJobLink": "https://www.indeed.com/rc/clk?jk=123abc456def"
  }
]

---

Directory Structure Tree

Indeed job scraper/
├── src/
│   ├── main.py
│   ├── crawler/
│   │   ├── pagination.py
│   │   └── throttling.py
│   ├── parsers/
│   │   ├── listing_parser.py
│   │   └── salary_parser.py
│   ├── exporters/
│   │   ├── json_exporter.py
│   │   └── csv_exporter.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── inputs.sample.txt
│   └── sample.json
├── tests/
│   ├── test_parsers.py
│   └── test_end_to_end.py
├── requirements.txt
└── README.md

---

Use Cases

• Talent acquisition teams use it to monitor fresh openings and employer activity, so they can source candidates faster and prioritize outreach.
• Market researchers use it to collect multi-region postings, so they can analyze hiring demand, salary bands, and role trends.
• Sales/BD teams use it to identify companies actively hiring specific roles, so they can generate qualified leads for B2B products/services.
• Data engineers use it to feed normalized listings into warehouses, so they can power dashboards and downstream ML pipelines.
• Educators and bootcamps use it to track skill requirements across roles, so they can update curricula based on real market demand.

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FAQs

Q1: What input do I need to start? Provide a complete Indeed search URL (including your filters like title, company, location, salary, job type). Optionally set a maximum results limit and region/proxy preferences.

Q2: How accurate is salary parsing? Salary ranges are parsed from visible snippets. When ranges or cadence are ambiguous, the raw text is preserved and numeric fields may be null, enabling your own post-processing rules.

Q3: Can it distinguish remote, hybrid, and onsite roles? Yes, when the listing exposes these tags or text patterns. The remoteWorkModel.type field is populated when detectable; otherwise the field is omitted.

Q4: How do I avoid duplicates across runs? Use the jobkey as a stable identifier. Store processed keys and skip already-seen listings when re-crawling overlapping searches.

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Performance Benchmarks and Results

Primary Metric: Processes ~1,000–1,500 listings per minute on typical broadband for paginated searches with moderate filtering. Reliability Metric: >98% successful page retrieval across long runs with retry and backoff enabled. Efficiency Metric: <300 KB average memory footprint per listing during parse/export; streaming exporters keep peak RAM low. Quality Metric: 95–99% field completeness on common attributes (title, company, location, link); 70–90% structured salary coverage when employers disclose ranges.

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