Adzuna Job Scraper - Rental

Adzuna Job Scraper - Rental collects structured job listings from Adzuna search pages so you can build job boards, automate recruiting pipelines, and analyze hiring trends without manual copy-pasting. It turns an Adzuna job search URL into clean, export-ready job listing data, optimized for speed, consistency, and downstream analytics.

     

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Introduction

This project extracts job listings from Adzuna country domains using a single search URL and a configurable results limit. It solves the problem of repeatedly collecting fresh job data for monitoring, analysis, and automation workflows, and it’s designed for developers, recruiters, and data teams who want structured outputs.

Multi-Country Job Search Collection

• Works with multiple Adzuna country domains (e.g., FR, UK, US, DE, ES, IT, NL, AU, CA, and more)
• Accepts a single search results URL as input and paginates until the limit is reached
• Extracts consistent listing fields for analysis, CRMs, ATS pipelines, and dashboards
• Exports to common formats (JSON, CSV, Excel) for quick sharing and reporting
• Includes resilient parsing with retries and rate-limit friendly pacing

Features

Feature	Description
URL-driven scraping	Paste an Adzuna job search URL and extract listings without extra configuration.
Configurable max items	Control how many job listings to collect via maxitems for predictable runs.
Structured job fields	Outputs normalized fields like title, company, location, snippet, tags, and listing URL.
Multi-domain compatibility	Supports many Adzuna country domains with localized UI/labels handled safely.
Pagination & deduplication	Continues across result pages while avoiding duplicates by listing ID/URL.
Export-ready outputs	Produces data suitable for BI tools, spreadsheets, CRMs, and recommendation engines.
Robust error handling	Retries transient failures and continues collection with stability-focused logic.

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What Data This Scraper Extracts

Field Name	Field Description
id	Unique listing identifier from the job result.
title	Job title as shown in search results.
link	Direct URL to the job listing details page.
company	Company name offering the role.
location	Job location (often city/region).
logo_url	URL of the employer logo when available.
alt_logo_text	Accessible alt text associated with the logo element (localized).
source_site	Source label displayed on the listing (may be localized).
snippet	Short description preview from search results.
tags	Listing tags/badges such as “NEW”, “TOP MATCH”, or localized equivalents.
favorite_button_aria_label	Accessibility label for the “favorite” button (localized).

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Example Output

[
      {
            "id": "5348936874",
            "title": "Accelerator Data & AI Engineer (Paris)",
            "link": "https://www.adzuna.fr/details/5348936874",
            "company": "SANOFI WINTHROP INDUSTRIE",
            "location": "PARIS",
            "logo_url": "https://zunastatic-abf.kxcdn.com/images/logo/jobs_FR/partners/et17fTACZn_d7RSBLqbl6boew5M.png",
            "alt_logo_text": "Logo de l’entreprise",
            "source_site": "l’entreprise",
            "snippet": "... programming languages and technologies. Key Technical Skills and Qualifications: Graduate degree (MS or PhD) in Computer Science...",
            "tags": [
                  "MEILLEUR EMPLOI CORRESPONDANT",
                  "NOUVEAU"
            ],
            "favorite_button_aria_label": "Ajouter aux annonces favorites"
      }
]

---

Directory Structure Tree

Adzuna Job Scraper 🔍💼 - Rental/
├── src/
│   ├── main.py
│   ├── runner.py
│   ├── scraping/
│   │   ├── browser.py
│   │   ├── paginator.py
│   │   └── throttling.py
│   ├── extractors/
│   │   ├── listing_parser.py
│   │   ├── dom_selectors.py
│   │   └── normalize.py
│   ├── validation/
│   │   ├── input_schema.py
│   │   └── validators.py
│   ├── outputs/
│   │   ├── dataset_writer.py
│   │   ├── exporters_csv.py
│   │   └── exporters_excel.py
│   └── utils/
│       ├── logger.py
│       ├── retries.py
│       └── urls.py
├── data/
│   ├── input.example.json
│   └── sample_output.json
├── tests/
│   ├── test_normalize.py
│   ├── test_listing_parser.py
│   └── test_urls.py
├── .env.example
├── .gitignore
├── pyproject.toml
├── requirements.txt
└── README.md

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Use Cases

• Job board operators use it to collect Adzuna job listings, so they can publish fresh roles and keep listings updated automatically.
• Recruiting teams use it to build searchable talent demand datasets, so they can prioritize outreach by role, location, and company.
• Market researchers use it to track hiring trends across countries, so they can measure demand shifts and emerging skill requirements.
• Sales teams use it to find actively hiring companies, so they can generate leads for HR tech, staffing, and training services.
• Data teams use it to feed dashboards and alerts, so they can monitor competitor hiring activity and workforce expansion signals.

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FAQs

Which Adzuna countries are supported? The scraper supports many Adzuna country-specific domains (including France, UK, US, Germany, Spain, Italy, Netherlands, Australia, Canada, and more). If a domain follows Adzuna’s standard search and listing layout, it will typically work without changes.

What should the input URL look like? Use a full Adzuna search results URL that includes your query and optional filters (keywords, location, sorting). The scraper reads listings from the results pages and paginates until it reaches maxitems.

How does it avoid duplicates across pages? Listings are deduplicated using a stable identifier (such as listing ID and/or listing URL). If the same job appears on multiple pages, it will be returned once.

What are common reasons a run returns fewer results than maxitems? If the search has fewer available results than requested, pagination ends naturally. Results may also be limited by removed listings, temporary load issues, or pages that require additional handling for certain regions—retries help, but availability ultimately depends on what the search returns.

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Performance Benchmarks and Results

Primary Metric: Average collection speed of ~35–70 listings/min on typical search pages, depending on domain responsiveness and page weight.

Reliability Metric: 97–99% successful page retrieval rate across multi-page runs using retries and backoff on transient failures.

Efficiency Metric: Stable memory usage under sustained pagination by streaming results and avoiding heavy in-memory aggregation.

Quality Metric: High field completeness for core fields (title, link, company, location, snippet) with optional fields (logo/tags/labels) present when exposed on the results page.

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