[Blog](https://scrapfly.io/blog) / [python](https://scrapfly.io/blog/tag/python) / [How to Scrape Indeed.com (2026 Update)](https://scrapfly.io/blog/posts/how-to-scrape-indeedcom) # How to Scrape Indeed.com (2026 Update) by [Bernardas Alisauskas](https://scrapfly.io/blog/author/bernardas) Apr 10, 2026 14 min read [\#python](https://scrapfly.io/blog/tag/python) [\#scrapeguide](https://scrapfly.io/blog/tag/scrapeguide) [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fscrapfly.io%2Fblog%2Fposts%2Fhow-to-scrape-indeedcom "Share on LinkedIn") In this web scraping tutorial, we'll take a look at how to scrape job listing data from [Indeed.com](https://www.indeed.com/). As one of the most popular job listing websites, Indeed remains a valuable data source in 2026 — though it now uses Cloudflare protection that requires proper anti-bot handling. In this tutorial, we'll build our scraper with just a few lines of Python code. We'll take a look at how Indeed's search works to replicate it in our scraper and extract job data from embedded javascript variables. Let's dive in! ## Key Takeaways Master indeed scraper development using Python with httpx and parsel, extracting job listings from embedded JavaScript variables and handling Indeed's search parameters for comprehensive job data collection. - Reverse engineer Indeed's search API endpoints by intercepting browser network requests and analyzing JSON response structures - Parse embedded JavaScript variables containing job data using regex patterns and JSON extraction techniques - Implement search parameter management and pagination handling for comprehensive job data collection across multiple pages - Extract structured job information including titles, companies, locations, and salary data from JSON responses - Handle Indeed's anti-scraping measures with realistic headers and request spacing to avoid detection - Configure concurrent request processing and response parsing for efficient large-scale job data extraction [**View Source Code**github.com/scrapfly/scrapfly-scrapers/tree/main/indeed-scraper](https://github.com/scrapfly/scrapfly-scrapers/tree/main/indeed-scraper) **Get web scraping tips in your inbox**Trusted by 100K+ developers and 30K+ enterprises. Unsubscribe anytime. ## Why Scrape Indeed.com? The job market is dynamic, featuring new opportunities and updates daily. Scraping Indeed allows for receiving real-time updates for different job postings in different industries and locations. Scraping Indeed also allows for studying the job market trends. This is achieved by aggregating the job data to identify patterns, in-demand skills and job requirements. Moreover, manually exploring thousands of job listings on the website can be time-consuming. However, we can quickly scrape Indeed for these listings or set up custom job posting alerts. ## Project Setup For this web scraper, we'll only need an HTTP client library such as [httpx](https://pypi.org/project/httpx/), which can be installed through `pip` console command: shell```shell $ pip install httpx ``` There are many HTTP clients in Python like requests, httpx, aiohttp, etc. However, we recommend httpx as it's the least one likely to be blocked, as it supports http2 protocol. Moreover, httpx also allows us to execute our web scraping code asynchronously, increasing our [web scraping speed](https://scrapfly.io/blog/posts/web-scraping-speed). For ScrapFly users, we'll also be providing code versions using [scrapfly-sdk](https://scrapfly.io/docs/sdk/python). ## Finding Indeed Jobs To start, let's take a look at how we can find job listings on [Indeed.com](https://indeed.com/). Go to the website homepage, submit a search, and you will be redirected to a search URL with a few key parameters: text```text https://www.indeed.com/jobs?q=python&l=Texas ``` So, for example, to find Python jobs in Texas, all we have to do is send a request with `l=Texas` and `q=Python` URL parameters: Python ScrapFly python```python import httpx HEADERS = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36", "Accept-Encoding": "gzip, deflate, br", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8", "Connection": "keep-alive", "Accept-Language": "en-US,en;q=0.9,lt;q=0.8,et;q=0.7,de;q=0.6", } response = httpx.get("https://www.indeed.com/jobs?q=python&l=Texas", headers=HEADERS) print(response) ``` python```python from scrapfly import ScrapflyClient, ScrapeConfig scrapfly = ScrapflyClient(key="YOUR SCRAPFLY KEY") result = scrapfly.scrape(ScrapeConfig( url="https://www.indeed.com/jobs?q=python&l=Texas", asp=True, )) print(result.selector.xpath('//h1').get()) ``` Note: if you receive response status code 403 here, it's likely you are being blocked. Run the ScrapFly code tabs to avoid blocking. We got a single page that contains 15 job listings! Before we collect the remaining pages, let's see how we can parse job listing data from this response. We could parse the HTML document using [Parsing HTML with CSS Selectors](https://scrapfly.io/blog/posts/parsing-html-with-css) or [Parsing HTML with Xpath](https://scrapfly.io/blog/posts/parsing-html-with-xpath) selectors, but there's an easier way: we can find all of the job listing data hidden away deep in the HTML as a JSON document: This type of data is commonly known as [hidden web data](https://scrapfly.io/blog/posts/how-to-scrape-hidden-web-data). It is the same data present on the web page but before it gets rendered in HTML. So, let's parse this data using a simple regular expression pattern: Python ScrapFly python```python import httpx import re import json HEADERS = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36", "Accept-Encoding": "gzip, deflate, br", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8", "Connection": "keep-alive", "Accept-Language": "en-US,en;q=0.9,lt;q=0.8,et;q=0.7,de;q=0.6", } def parse_search_page(html: str): data = re.findall(r'window.mosaic.providerData\["mosaic-provider-jobcards"\]=(\{.+?\});', html) data = json.loads(data[0]) return { "results": data["metaData"]["mosaicProviderJobCardsModel"]["results"], "meta": data["metaData"]["mosaicProviderJobCardsModel"]["tierSummaries"], } response = httpx.get("https://www.indeed.com/jobs?q=python&l=Texas", headers=HEADERS) print(parse_search_page(response.text)) ``` python```python import re import json from scrapfly import ScrapflyClient, ScrapeConfig scrapfly = ScrapflyClient(key="YOUR SCRAPFLY KEY") def parse_search_page(html: str): data = re.findall(r'window.mosaic.providerData\["mosaic-provider-jobcards"\]=(\{.+?\});', html) data = json.loads(data[0]) return { "results": data["metaData"]["mosaicProviderJobCardsModel"]["results"], "meta": data["metaData"]["mosaicProviderJobCardsModel"]["tierSummaries"], } result = scrapfly.scrape( ScrapeConfig( url="https://www.indeed.com/jobs?q=python&l=Texas", asp=True, ) ) print(parse_search_page(result.content)) ``` In our code above, we are using a regular expression pattern to select `mosaic-provider-jobcards` variable value, load it as a python dictionary and parse out the result and paging meta-data. Now that we have the first page results and total page count, we can retrieve the remaining pages: Python ScrapFly python```python import asyncio import httpx import json import re from urllib.parse import urlencode def parse_search_page(html: str): data = re.findall(r'window.mosaic.providerData\["mosaic-provider-jobcards"\]=(\{.+?\});', html) data = json.loads(data[0]) return { "results": data["metaData"]["mosaicProviderJobCardsModel"]["results"], "meta": data["metaData"]["mosaicProviderJobCardsModel"]["tierSummaries"], } async def scrape_search(client: httpx.AsyncClient, query: str, location: str, max_results: int = 50): def make_page_url(offset): parameters = {"q": query, "l": location, "filter": 0, "start": offset} return "https://www.indeed.com/jobs?" + urlencode(parameters) print(f"scraping first page of search: {query=}, {location=}") response_first_page = await client.get(make_page_url(0)) data_first_page = parse_search_page(response_first_page.text) results = data_first_page["results"] total_results = sum(category["jobCount"] for category in data_first_page["meta"]) # there's a page limit on indeed.com of 1000 results per search if total_results > max_results: total_results = max_results print(f"scraping remaining {total_results - 10 / 10} pages") other_pages = [make_page_url(offset) for offset in range(10, total_results + 10, 10)] for response in await asyncio.gather(*[client.get(url=url) for url in other_pages]): results.extend(parse_search_page(response.text)) return results ``` python```python import json import re from urllib.parse import urlencode from scrapfly import ScrapflyClient, ScrapeConfig scrapfly = ScrapflyClient(key="YOUR SCRAPFLY KEY") def parse_search_page(html: str): data = re.findall(r'window.mosaic.providerData\["mosaic-provider-jobcards"\]=(\{.+?\});', html) data = json.loads(data[0]) return { "results": data["metaData"]["mosaicProviderJobCardsModel"]["results"], "meta": data["metaData"]["mosaicProviderJobCardsModel"]["tierSummaries"], } async def scrape_search(query: str, location: str, max_results: int = 50): def make_page_url(offset): parameters = {"q": query, "l": location, "filter": 0, "start": offset} return "https://www.indeed.com/jobs?" + urlencode(parameters) print(f"scraping first page of search: {query=}, {location=}") result_first_page = await scrapfly.async_scrape(ScrapeConfig(make_page_url(0), asp=True)) data_first_page = parse_search_page(result_first_page.content) results = data_first_page["results"] total_results = sum(category["jobCount"] for category in data_first_page["meta"]) # there's a page limit on indeed.com of 1000 results per search if total_results > max_results: total_results = max_results print(f"scraping remaining {total_results - 10 / 10} pages") other_pages = [ ScrapeConfig(make_page_url(offset), asp=True) for offset in range(10, total_results + 10, 10) ] async for result in scrapfly.concurrent_scrape(other_pages): results.extend(parse_search_page(result.content)) return results # example run import asyncio asyncio.run(scrape_search(query="python", location="texas")) ``` Run Code & Example Outputpython```python async def main(): # we need to use browser-like headers to avoid being blocked instantly: HEADERS = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36", "Accept-Encoding": "gzip, deflate, br", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8", "Accept-Language": "en-US,en;q=0.9,lt;q=0.8,et;q=0.7,de;q=0.6", } async with httpx.AsyncClient(headers=HEADERS) as client: search_data = await scrape_search(client, query="python", location="texas") print(json.dumps(search_data, indent=2)) asyncio.run(main()) ``` The search result data is similar to the following: json```json [ { "company": "Apple", "companyBrandingAttributes": { "headerImageUrl": "https://d2q79iu7y748jz.cloudfront.net/s/_headerimage/1960x400/ecdb4796986d27b654fe959e2fdac201", "logoUrl": "https://d2q79iu7y748jz.cloudfront.net/s/_squarelogo/256x256/86583e966849b2f081928769a6abdb09" }, "companyIdEncrypted": "c1099851e9794854", "companyOverviewLink": "/cmp/Apple", "companyOverviewLinkCampaignId": "serp-linkcompanyname", "companyRating": 4.1, "companyReviewCount": 11193, "companyReviewLink": "/cmp/Apple/reviews", "companyReviewLinkCampaignId": "cmplinktst2", "displayTitle": "Software Quality Engineer, Apple Pay", "employerAssistEnabled": false, "employerResponsive": false, "encryptedFccompanyId": "6e7b40121fbb5e2f", "encryptedResultData": "VwIPTVJ1cTn5AN7Q-tSqGRXGNe2wB2UYx73qSczFnGU", "expired": false, "extractTrackingUrls": "https://jsv3.recruitics.com/partner/a51b8de1-f7bf-11e7-9edd-d951492604d9.gif?client=3427&rx_c=&rx_campaign=indeed16&rx_group=130795&rx_source=Indeed&job=200336736-2&rx_r=none&rx_ts=20220831T001748Z&rx_pre=1&indeed=sp", "extractedEntities": [], "fccompanyId": -1, "featuredCompanyAttributes": {}, "featuredEmployer": false, "featuredEmployerCandidate": false, "feedId": 2772, "formattedLocation": "Austin, TX", "formattedRelativeTime": "Today", "hideMetaData": false, "hideSave": false, "highVolumeHiringModel": { "highVolumeHiring": false }, "highlyRatedEmployer": false, "hiringEventJob": false, "indeedApplyEnabled": false, "indeedApplyable": false, "isJobSpotterJob": false, "isJobVisited": false, "isMobileThirdPartyApplyable": true, "isNoResumeJob": false, "isSubsidiaryJob": false, "jobCardRequirementsModel": { "additionalRequirementsCount": 0, "requirementsHeaderShown": false }, "jobLocationCity": "Austin", "jobLocationState": "TX", "jobTypes": [], "jobkey": "5b47456ae8554711", "jsiEnabled": false, "locationCount": 0, "mobtk": "1gbpe4pcikib6800", "moreLocUrl": "", "newJob": true, "normTitle": "Software Quality Engineer", "openInterviewsInterviewsOnTheSpot": false, "openInterviewsJob": false, "openInterviewsOffersOnTheSpot": false, "openInterviewsPhoneJob": false, "overrideIndeedApplyText": true, "preciseLocationModel": { "obfuscateLocation": false, "overrideJCMPreciseLocationModel": true }, "pubDate": 1661835600000, "redirectToThirdPartySite": false, "remoteLocation": false, "resumeMatch": false, "salarySnippet": { "salaryTextFormatted": false }, "saved": false, "savedApplication": false, "showCommutePromo": false, "showEarlyApply": false, "showJobType": false, "showRelativeDate": true, "showSponsoredLabel": false, "showStrongerAppliedLabel": false, "smartFillEnabled": false, "snippet": "", "sourceId": 2700, "sponsored": true, "taxoAttributes": [], "taxoAttributesDisplayLimit": 5, "taxoLogAttributes": [], "taxonomyAttributes": [ { "attributes": [], "label": "job-types" }, "..."], "tier": { "matchedPreferences": { "longMatchedPreferences": [], "stringMatchedPreferences": [] }, "type": "DEFAULT" }, "title": "Software Quality Engineer, Apple Pay", "translatedAttributes": [], "translatedCmiJobTags": [], "truncatedCompany": "Apple", "urgentlyHiring": false, "viewJobLink": "...", "vjFeaturedEmployerCandidate": false }, ] ``` We've successfully scraped mountains of data with very few lines of Python code! Let's learn how to scrape job pages to obtain the remaining details of a job listing, such as the full description. ## Scraping Indeed Jobs Our search results contain almost all job listing data except a few details, such as a complete job description. To scrape this, we need the job id, which is found in the `jobkey` field in our search results: json```json { "jobkey": "a82cf0bd2092efa3", } ``` Using `jobkey` we can request the full job details page, and just like with the search, we can parse the hidden data instead of the HTML: We can see that all of the job and page information is hidden in the `_initialData` variable. It can be extracted with a simple regular expression pattern: Python ScrapFly python```python import re import json import httpx import asyncio from typing import List def parse_job_page(html): """parse job data from job listing page""" data = re.findall(r"_initialData=(\{.+?\});", html) data = json.loads(data[0]) return data["jobInfoWrapperModel"]["jobInfoModel"] async def scrape_jobs(client: httpx.AsyncClient, job_keys: List[str]): """scrape job details from job page for given job keys""" urls = [f"https://www.indeed.com/m/basecamp/viewjob?viewtype=embedded&jk={job_key}" for job_key in job_keys] scraped = [] for response in await asyncio.gather(*[client.get(url=url) for url in urls]): scraped.append(parse_job_page(response.text)) return scraped ``` python```python import re import json from typing import List from scrapfly import ScrapeConfig, ScrapflyClient scrapfly = ScrapflyClient(key="YOUR SCRAPFLY KEY") def parse_job_page(html): """parse job data from job listing page""" data = re.findall(r"_initialData=(\{.+?\});", html) data = json.loads(data[0]) return data["jobInfoWrapperModel"]["jobInfoModel"] async def scrape_jobs(job_keys: List[str]): """scrape job details from job page for given job keys""" urls = [f"https://www.indeed.com/m/basecamp/viewjob?viewtype=embedded&jk={job_key}" for job_key in job_keys] to_scrape = [ScrapeConfig(url=url, asp=True) for url in urls] scraped = [] async for result in scrapfly.concurrent_scrape(to_scrape): scraped.append(parse_job_page(result.content)) return scraped ``` Run Code & Example Outputpython```python async def main(): HEADERS = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36", "Accept-Encoding": "gzip, deflate, br", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8", "Connection": "keep-alive", "Accept-Language": "en-US,en;q=0.9,lt;q=0.8,et;q=0.7,de;q=0.6", } async with httpx.AsyncClient(headers=HEADERS) as client: job_data = await scrape_jobs(client, ["9100493864fe1d6e", "5361f22542fe4a95"]) print(job_data[0]['sanitizedJobDescription']['content']) print(job_data) asyncio.run(main()) ``` This will scrape results similar to: json```json [ { "jobInfoHeaderModel": { "...", "companyName": "ExxonMobil", "companyOverviewLink": "https://www.indeed.com/cmp/Exxonmobil?campaignid=mobvjcmp&from=mobviewjob&tk=1gbpekba3is92800&fromjk=9dacdef3068a1d25", "companyReviewLink": "https://www.indeed.com/cmp/Exxonmobil/reviews?campaignid=mobvjcmp&cmpratingc=mobviewjob&from=mobviewjob&tk=1gbpekba3is92800&fromjk=9dacdef3068a1d25&jt=Geoscience+Technician", "companyReviewModel": { "companyName": "ExxonMobil", "desktopCompanyLink": "https://www.indeed.com/cmp/Exxonmobil/reviews?campaignid=viewjob&cmpratingc=mobviewjob&from=viewjob&tk=1gbpekba3is92800&fromjk=9dacdef3068a1d25&jt=Geoscience+Technician", "mobileCompanyLink": "https://www.indeed.com/cmp/Exxonmobil/reviews?campaignid=mobvjcmp&cmpratingc=mobviewjob&from=mobviewjob&tk=1gbpekba3is92800&fromjk=9dacdef3068a1d25&jt=Geoscience+Technician", "ratingsModel": { "ariaContent": "3.9 out of 5 stars from 4,649 employee ratings", "count": 4649, "countContent": "4,649 reviews", "descriptionContent": "Read what people are saying about working here.", "rating": 3.9, "showCount": true, "showDescription": true, "size": null } }, "disableAcmeLink": false, "employerActivity": null, "employerResponsiveCardModel": null, "formattedLocation": "Spring, TX 77389", "hideRating": false, "isDesktopApplyButtonSticky": false, "isSimplifiedHeader": false, "jobTitle": "Geoscience Technician", "openCompanyLinksInNewTab": false, "parentCompanyName": null, "preciseLocationModel": null, "ratingsModel": null, "remoteWorkModel": null, "subtitle": "ExxonMobil - Spring, TX 77389", "tagModels": null, "viewJobDisplay": "DESKTOP_EMBEDDED" }, "sanitizedJobDescription": { "content": "

\n
\n
\n
\n
\n

Education and Related Experience

\n
\n
\n ...", "contentKind": "HTML" }, "viewJobDisplay": "DESKTOP_EMBEDDED" } ] ``` We should see the full job description printed out if we run this scraper. --- With this last feature, our indeed scraper is ready to go! However, our scraper is very likely to get blocked while running it at scale. For that, let's take a look at how we can integrate ScrapFly to avoid being blocked. Scrapfly #### Scale your web scraping effortlessly Scrapfly handles proxies, browsers, and anti-bot bypass — so you can focus on data. [Try Free →](https://scrapfly.io/register)## Bypass Indeed Blocking with ScrapFly Indeed.com is using anti-scraping protection to block web scraper traffic. To get around this, we can use ScrapFly web scraping API which will help you scale up! Learn more about [Web Scraping API](https://scrapfly.io/web-scraping-api) and how it works. For our Indeed scraper, we'll be using the [Anti Scraping Protection Bypass](https://scrapfly.io/docs/scrape-api/anti-scraping-protection) feature via [Scrapfly-sdk](https://scrapfly.io/docs/sdk/python), which can be installed using `pip` console command: shell```shell $ pip install scrapfly-sdk ``` Now, we can enable the Anti Scraping Protection bypass via the `asp=True` flag: python```python from scrapfly import ScrapflyClient, ScrapeConfig client = ScrapflyClient(key="YOUR_API_KEY") result = client.scrape(ScrapeConfig( url="https://www.indeed.com/jobs?q=python&l=Texas", asp=True, # ^ enable Anti Scraping Protection )) html = result.content # get the page HTML selector = result.selector # use the built-in parsel selector ``` ## FAQ Is it legal to scrape Indeed.com?Yes. The job data on Indeed.com is publicly available so it's perfectly legal to scrape. Note that some of the scraped material can be protected by copyright, such as images. Can Indeed be scraped using headless browsers such as Playwright?Yes, but as covered in this article it's not necessary. Indeed pages are powered by a JSON API which can be scraped directly. This reduces the resource requirement for both the scraper and Indeed.com public data servers. Is there a public API for Indeed.com?No. As of the time of writing, there is no public API for Indeed.com job data. However, as indicated by this article Indeed.com can be easily scraped using Python! [**View Source Code**github.com/scrapfly/scrapfly-scrapers/tree/main/indeed-scraper](https://github.com/scrapfly/scrapfly-scrapers/tree/main/indeed-scraper) ## Indeed Scraping Summary In this short web scraping tutorial, we've looked at web scraping [Indeed.com](https://indeed.com) job listing search. We built a search URL using custom search parameters and parsed job data from the embedded JSON data by using regular expressions. As a bonus, we also looked at scraping full job listing descriptions and how to avoid blocking using the Scrapfly SDK. Legal Disclaimer and PrecautionsThis tutorial covers popular web scraping techniques for education. Interacting with public servers requires diligence and respect: - Do not scrape at rates that could damage the website. - Do not scrape data that's not available publicly. - Do not store PII of EU citizens protected by GDPR. - Do not repurpose *entire* public datasets which can be illegal in some countries. Scrapfly does not offer legal advice but these are good general rules to follow. For more you should consult a lawyer. Table of Contents- [Key Takeaways](#key-takeaways) - [Why Scrape Indeed.com?](#why-scrape-indeed-com) - [Project Setup](#project-setup) - [Finding Indeed Jobs](#finding-indeed-jobs) - [Scraping Indeed Jobs](#scraping-indeed-jobs) - [Bypass Indeed Blocking with ScrapFly](#bypass-indeed-blocking-with-scrapfly) - [FAQ](#faq) - [Indeed Scraping Summary](#indeed-scraping-summary) Join the Newsletter Get monthly web scraping insights Scale Your Web Scraping Anti-bot bypass, browser rendering, and rotating proxies, all in one API. Start with 1,000 free credits. No credit card required 1,000 free API credits Anti-bot bypass included [Start Free](https://scrapfly.io/register) [View Docs](https://scrapfly.io/docs/onboarding) Not ready? 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