Indeed Scraper

Scrape Indeed from Python or the command line: job listings for a role and a location, with the salary as Indeed prints it and as numbers (minimum, maximum, period, currency), benefits, schedules, remote model, employer rating and posting date, as JSON or CSV. The code is free and open source (MIT). It runs on the QuanticData Data API and reads public listings only, so there is no Indeed account, browser extension, proxy or headless browser involved: sign in to QuanticData once and run a command. The same collector works from Claude or any MCP client, and from n8n.

pip install git+https://github.com/quantumproxies/indeed-scraper
indeed-scraper login
indeed-scraper jobs "data engineer" --location "New York" --country us --max-results 50 -o jobs.csv

login opens the QuanticData sign-in page in your browser. New accounts get $2 of free usage every month, no card required: create one.

What it scrapes

Command	Input	Returns	Per run
indeed-scraper jobs	role, optional location	Title, company, location, salary range, benefits, posting date, 30 fields per row (fields and price)	up to 300

--country picks the local Indeed site (default us): indeed-scraper jobs "sviluppatore backend" --location Milano --country it searches the Italian one. Narrow the listings with --location (city, state or postcode) and --posted-within-days (1 to 30). Every salary comes back twice: as Indeed prints it (salary_text) and as numbers in salary_min, salary_max, salary_period and salary_currency.

Sample output

A real row from indeed-scraper jobs "data engineer" --location "New York" --country us --max-results 3 (2026-10-23):

{
  "city": "New York",
  "link": "https://www.indeed.com/viewjob?jk=497fe5a6dfd14dec",
  "rank": 1,
  "state": "NY",
  "title": "Data Engineer",
  "posted": "30+ days ago",
  "remote": "REMOTE_HYBRID",
  "salary": "115000 - 130000 yearly",
  "company": "TheGuarantors",
  "job_key": "497fe5a6dfd14dec",
  "new_job": false,
  "snippet": "Collaboration: Collaborate with cross-functional teams including data analysts, data scientists, and business stakeholders to understand data requirements and…",
  "benefits": [
    "Health savings account",
    "Dental insurance",
    "401(k)",
    "Flexible spending account",
    "Disability insurance",
    "Vision insurance",
    "Parental leave",
    "Health insurance",
    "Paid parental leave",
    "Flexible schedule",
    "Paid time off"
  ],
  "location": "New York, NY 10007",
  "job_types": [
    "Full-time"
  ],
  "posted_at": "2026-08-25",
  "schedules": [],
  "sponsored": false,
  "easy_apply": true,
  "salary_max": 130000,
  "salary_min": 115000,
  "company_url": "https://www.indeed.com/cmp/Theguarantors",
  "postal_code": "10007",
  "salary_text": "$115,000 - $130,000 a year",
  "requirements": [],
  "salary_period": "yearly",
  "company_rating": 2.8,
  "company_reviews": 4,
  "salary_currency": "USD",
  "urgently_hiring": false
}

Python

from quanticdata_indeed_scraper import IndeedScraper

indeed = IndeedScraper()  # saved login, or QUANTICDATA_API_KEY

jobs = indeed.jobs("data engineer", location="New York", country="us", max_results=50)
for j in jobs:
    print(j["posted_at"], j["company"], j["salary_text"], j["title"])
jobs.to_csv("data_engineer_jobs.csv")

recent = indeed.jobs("sviluppatore backend", location="Milano", country="it", posted_within_days=14)
yearly = [j for j in recent if j["salary_period"] == "yearly"]
print(len(recent), "listings,", len(yearly), "with a yearly salary")

Each call returns a Run: iterate it for the rows (plain dicts), take len(), or save it with to_csv(), to_json(), to_jsonl() or to_dataframe() (pandas). Errors are specific: InsufficientCredit, RateLimited, InvalidInput, JobFailed, AuthError.

Examples

• Salary benchmarks: Run one role in several cities and get the median advertised salary range per city, computed from the numeric salary fields, plus a CSV of every listing behind it. See examples/salary_benchmark.py.
• New-listing log: Check a search every morning and append only the listings you have not seen before, keyed by Indeed's job key, to one growing CSV. See examples/new_listings.py.
• Who is hiring: Count the listings per employer for a few roles in one location, with each employer's Indeed rating, review count and company page. See examples/hiring_companies.py.

Command line

Results print as a table; -o file.csv, -o file.json or -o file.jsonl writes a file. List inputs take a b c, a,b,c, @file.txt (one per line) or - (stdin). --no-wait starts a long run in the background and prints its id; indeed-scraper run <id> -o out.csv collects it later.

indeed-scraper jobs

Indeed job listings for a role and a location, with structured salary ranges, from the Indeed site of the country you pick.

indeed-scraper jobs QUERY [options]

Option	Type	Default	Description
QUERY	string		Job title or keywords, e.g. "data engineer".
--location	string		City, state or postcode, e.g. "New York".
--posted-within-days	integer		Only listings posted in the last N days (1–30).
--country	string		Which Indeed site to search, as an ISO country code: us (default) or another market's code, e.g. it for Italy.
--max-results	integer	25	How many jobs to deliver at most (1–300). You pay only for delivered jobs.

Output fields

indeed-scraper jobs: 30 fields

Field	Type	Description
rank	integer	1-based position across pages.
job_key	string	Indeed job key (dedupe key).
title	string	Job title.
company	string or null	Employer.
location	string or null	Location as shown.
remote	string or null	Remote model (REMOTE, REMOTE_HYBRID…).
salary	string or null	Salary as a readable string.
salary_min	number or null	Salary range minimum.
salary_max	number or null	Salary range maximum.
salary_period	string or null	yearly / monthly / hourly.
job_types	string[]	Employment types when tagged.
posted	string or null	Posting age ("11 days ago").
snippet	string or null	Listing summary, HTML stripped.
company_rating	number or null	Employer rating on Indeed.
company_reviews	integer or null	Employer review count.
sponsored	boolean	True for paid placements.
link	string	Listing URL.
posted_at	string or null	Publication date (YYYY-MM-DD).
salary_text	string or null	Salary as Indeed prints it ("$175,000 - $260,000 a year").
salary_currency	string or null	ISO currency of the salary (USD, EUR, GBP…).
city	string or null	Job city.
state	string or null	State / region code.
postal_code	string or null	Postal code when the listing states one.
benefits	string[]	Benefits the employer tagged (health insurance, 401(k), paid time off…).
schedules	string[]	Schedule and shift tags (Monday to Friday, 8 hour shift…).
requirements	string[]	Skills and requirements listed on the card (SQL, Python, AWS…).
company_url	string or null	Employer page on Indeed.
easy_apply	boolean	True when the listing accepts an application on Indeed.
urgently_hiring	boolean	True when the employer marked the role as urgent.
new_job	boolean	True when Indeed flags the listing as new.

Sign-in and API keys

• indeed-scraper login signs you in with OAuth 2.1 and PKCE in the browser. The token is stored in ~/.config/quanticdata/credentials.json (readable only by you) and refreshed automatically. One login serves every QuanticData tool on the machine.
• No browser on the machine (SSH, Docker, Colab): run indeed-scraper login --no-browser, open the link anywhere, then paste back the address the browser ends on.
• Scripts and CI: set QUANTICDATA_API_KEY to a key from the dashboard, or store one with indeed-scraper login --api-key.
• indeed-scraper logout revokes the token. The connection is listed under API keys in the dashboard as "API · QuanticData CLI"; deleting that key disconnects every machine that uses it.

Pricing

The code is free and MIT-licensed. Runs are billed to your QuanticData account per delivered row, at the price shown on each collector page linked above; $2 of usage is free every month and failed runs are never billed. Plans with higher limits: pricing.

Other ways to call it

The same data over plain HTTP:

curl -X POST https://api.quanticdata.io/v1/scraper/collectors/indeed_jobs/run \
  -H "Authorization: Bearer $QUANTICDATA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"input": {"query": "data engineer", "location": "New York", "country": "us", "max_results": 3}}'

• API reference with Python, Node, PHP and curl examples.
• MCP server to give Claude, Cursor or any MCP client the same tools.
• n8n node for no-code workflows.

FAQ

Is this Indeed scraper free?

The code is free and MIT-licensed. Scraping runs on your QuanticData account: $2 of usage is free every month with no card, then you pay per delivered row at the price shown on each collector page. Failed runs are never billed.

Do I need proxies or a headless browser?

No. You send a role and a location and get rows back; the requests run on QuanticData's side. There is no browser extension to install and no Indeed account to connect: it reads the public listings anyone can open.

Does it work outside the US?

Yes. --country picks the local Indeed site (default us); --country it with --location Milano searches the Italian one, for example. salary_currency tells you the currency of each salary.

How many jobs can one run return?

Up to 300 job listings per run; ask for fewer with --max-results and you pay only for what is delivered.

Can I export to CSV or Excel?

Add -o results.csv (or .json, .jsonl). Lists such as benefits, schedules and requirements are joined with '; ', so the CSV opens cleanly in Excel or Google Sheets.

Can I run it on a schedule?

Yes. Set QUANTICDATA_API_KEY in the environment and call the CLI or examples/new_listings.py from cron, GitHub Actions or any scheduler.

Related scrapers

• LinkedIn Scraper: LinkedIn scraper for Python and the command line.
• Wellfound Scraper: Wellfound scraper for Python and the command line.
• Google SERP API: Google SERP API for Python and the command line.

License

MIT. Built and maintained by QuanticData.