FAANG Job Search Scraper

Scrape job listings from Meta, Amazon, Apple, Netflix, and Google career sites in a single run. Give the FAANG Job Search Scraper a keyword and (optionally) a location, pick which companies to check, and it returns structured job postings — title, location, team, posting date, full description, responsibilities, qualifications, and pay/benefits where each site publishes them. Try it with a query like "software engineer" and see live job listings pulled straight from each company's own career site.

Instead of hitting rendered HTML pages, the Actor talks directly to each company's internal search API (or, for Google, the embedded JSON payload behind the rendered page) — faster and less brittle than screen-scraping. Running it on Apify gets you scheduling, an API to pull results programmatically, proxy rotation for the sites that need it, and monitoring if a site's API ever changes shape.

Why use this scraper?

• Multi-company job search in one place — compare openings for the same role across five of the largest tech employers without visiting five different sites.
• Structured data for analysis — feed results into a spreadsheet, dashboard, or alerting pipeline instead of manually copy-pasting job posts.
• Full posting text, not just titles — responsibilities, qualifications, and (where disclosed) pay ranges are included, so you don't need to open every listing individually.
• Recruiting and market research — track how many roles a company has open for a given skill set, or watch for new postings on a schedule.

How to use the FAANG Job Search Scraper

1. Click Try for free or set up a new run in the Apify Console.
2. Enter a search query (e.g. "data engineer") — this is required.
3. Optionally enter a location (e.g. "London", "Seattle"). Matching format varies by site — see the Input section below.
4. Pick one or more sources to scrape: Meta, Amazon, Apple, Netflix, Google.
5. Optionally set a max items per source cap (defaults to 100).
6. Click Start and view results in the Dataset tab once the run finishes, or pull them via the API.

Input

Field	Type	Description
query	string	Job title or keyword to search for. Required.
location	string	Office location or region filter. Optional — leave blank to search all locations.
sources	array	Which career sites to scrape: meta, amazon, apple, netflix, google. Select one or more.
maxItemsPerSource	integer	Maximum jobs to fetch per selected source (0 = unlimited). Default 100.
proxyConfiguration	object	Proxy settings. Apify Proxy is recommended, especially for Meta and Apple.

Example input:

{
  "query": "software engineer",
  "location": "London",
  "sources": ["meta", "amazon", "apple", "netflix", "google"],
  "maxItemsPerSource": 100
}

A note on location matching

Each site matches location differently — there's no universal format across all five:

• Amazon: free-text works loosely; exact country/region codes filter more precisely.
• Apple: matched against the site's own location autocomplete list; a bare city name that exists in multiple countries (e.g. "London") is disambiguated with a UK-first preference.
• Netflix: needs the exact geocoded string the site's own autocomplete would produce (e.g. "London, England, United Kingdom"); a bare city name may return zero results.
• Meta: matched against exact office names as listed on the careers site (e.g. "London, UK").
• Google: passed straight through as the site's own location query param.

If a source returns fewer results than expected, try the fuller location string as it appears on that company's own careers page, or leave location blank to search everywhere.

A note on search relevance (fuzzy matching)

This Actor sends your query to each site's own search backend and returns exactly what that site would show a human visitor — it does not re-rank or filter results itself. Some of these search backends (Apple's in particular) do fuzzy, relevance-based matching rather than literal phrase matching: a query like "web scraping" can return hundreds of loosely-related engineering roles that don't literally contain those words, ranked by the site's own relevance score rather than by keyword match. A specific listing you're looking for may exist further down the result list (not on the first page) — raise maxItemsPerSource if you're not finding something you expect to be there. This is inherent to how each career site's search works, not a bug in the scraper.

Output

Each dataset row is one job posting:

{
  "source": "apple",
  "job_id": "200672712-2114",
  "title": "iCloud Platform - Senior Software Engineer",
  "company": "Apple",
  "location": "London, England, United Kingdom",
  "team": "Software and Services",
  "url": "https://jobs.apple.com/en-gb/details/200672712-2114/icloud-platform-senior-software-engineer",
  "posted_date": "21 Jul 2026",
  "description": "Imagine what you could do here. At Apple, great ideas have a way of becoming phenomenal products...",
  "responsibilities": "Design and build large-scale distributed systems...",
  "minimum_qualifications": "Comprehensive relevant industry experience...",
  "preferred_qualifications": "Excellent problem solving and analytical thinking skills...",
  "pay_and_benefits": null,
  "query": "software engineer",
  "search_location": "London"
}

You can download the dataset in various formats such as JSON, HTML, CSV, or Excel.

Data fields

Field	Description
source	Which site the job came from (meta, amazon, apple, netflix, google)
job_id	Source-specific job/requisition ID
title	Job title
company	Company name
location	Job location as reported by the source
team	Team or department, where available
url	Link to the live job posting
posted_date	Posting date, where available
description	Full job description text
responsibilities	Responsibilities section, where the source separates it out
minimum_qualifications	Required/minimum qualifications, where available
preferred_qualifications	Preferred/nice-to-have qualifications, where available
pay_and_benefits	Base pay range and benefits text, where the source discloses it (mainly US roles with legally required pay transparency)
query	The search query you provided
search_location	The location filter you provided

Not every source populates every field — some sites (Netflix) return one combined description with everything inline rather than separate sections, and pay data is only published where legally required.

How much does it cost to scrape job listings?

This Actor uses pay-per-event pricing: you're charged $2 per 1,000 job listings returned in the dataset ($0.002 per row), billed only for rows actually delivered — a run that returns 250 jobs costs $0.50, and a run that returns nothing costs nothing. There's no separate per-run or per-source fee.

Apify platform usage (compute, proxy, data transfer) for the run is also passed through, on top of the per-listing charge. Use maxItemsPerSource to cap costs upfront — e.g. 100 items × 5 sources = up to 500 listings ≈ $1. Check the Pricing tab on this Actor's page for the current rate.

Tips for better results

• Start with a small maxItemsPerSource (10–20) to sanity-check your query and location before scaling up.
• Leave location blank for the broadest search, then narrow once you've confirmed the right format for the sources you care about.
• Select only the sources you need — running all five takes longer and costs more than running one or two.
• If a source returns unexpectedly few or irrelevant results, see the fuzzy-matching and location-format notes above before assuming something's broken.

FAQ

Is this legal? This Actor only reads publicly available job listing pages and their backing APIs — the same data a visitor to each career site can already see. You're responsible for using scraped data in line with each site's terms of service and applicable law.

Why do some fields come back empty? Each source publishes a different amount of structured detail. See the Data fields table above for what's available per source.

A job I expected isn't showing up — what's wrong? Usually one of two things: maxItemsPerSource is too low to reach it (career sites rank results by their own relevance, not always by recency), or the location string doesn't match that source's expected format. See the notes above.

Something looks broken or you have a feature request? Open an issue in this Actor's Issues tab, or reach out for a custom scraping solution.