How To Scrape Indeed Using Python

TL;DR

Build a Python 3 scraper for Indeed NY “Python” jobs using requests + BeautifulSoup.
Extract job title, company, location, and details by looping job cards; full code included.
Fine for demos / small runs; at scale you’ll hit blocks.
For scale, the post recommends Scrapingdog’s Indeed Scraper (JSON) with 1,000 free credits.

Indeed is one of the biggest job listing platforms available in the market. According to SemRush, they have monthly visitors ~ 542.52 Millions.

As a data engineer, you want to identify which job is in great demand. Well, then you have to scrape data from websites like Indeed to identify and make a conclusion.

In this article, we are going to web scrape Indeed & create a Scraper using Python 3.x. We are going to scrape Python jobs from Indeed in New York.

At the end of this tutorial, we will have all the jobs that need Python as a skill in New York.

Why Scrape Indeed Jobs?

Scraping Indeed Jobs can help you in multiple ways. Some of the use cases for extracting data from it are: –

With this much data, you can train an AI model to predict salaries in the future for any given skill.
Companies can use this data to analyze what salaries their rival companies are offering for a particular skill set. This will help them improve their recruitment strategy.
You can also analyze what jobs are in high demand and what kind of skill set one needs to qualify for jobs in the future.

Setting up the prerequisites

We would need Python 3.x for this project and our target page will be this one from Indeed.

I am assuming that you have already installed Python on your machine. So, let’s move forward with the rest of the installation.

We would need two libraries that will help us extract data. We will install them with the help of pip.

Requests — Using this library we are going to make a GET request to the target URL.
BeautifulSoup — Using this library we are going to parse HTML and extract all the crucial data that we need from the page. It is also known as BS4.

Installation

You can create a dedicated folder for Indeed on your machine and then create a Python file where we will write the code.

Let’s decide what we are going to scrape from Indeed.com

Whenever you start a scraping project, it is always better to decide in advance what exactly we need to extract from the target page.

We are going to scrape all the highlighted parts in the above image.

Name of the job
Name of the company
Location
Job details

Let’s Start Indeed Job Scraping

Before even writing the first line of code, let’s find the exact element location in the DOM.

Every job box is a list tag. You can see this in the above image. There are 18 of them on each page, all under the div tag with class jobsearch-ResultsList. So, our first job would be to find this div tag. Let’s first import all the libraries in the file.

1import requests
2from bs4 import BeautifulSoup
3 
4url = "https://api.scrapingdog.com/scrape"
5params = {
6    "api_key": "your-api-key",
7    "url": "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df",
8    "dynamic": "false"
9}
10 
11response = requests.get(url, params=params)
12 
13obj={}
14l=[]
15 
16if response.status_code == 200:
17    print("Success! Page fetched.")
18    soup = BeautifulSoup(response.text, "html.parser")
19 
20    job_listings = soup.find_all("li", class_="eu4oa1w0")
21 
22    for job in job_listings:
23        # Job name
24        title_tag = job.find("a", class_="jcs-JobTitle")
25        obj["title"] = title_tag.get_text(strip=True) if title_tag else None
26 
27        # Company name
28        company_tag = job.find("span", attrs={"data-testid": "company-name"})
29        obj["company"] = company_tag.get_text(strip=True) if company_tag else None
30 
31        # Location
32        location_tag = job.find("div", attrs={"data-testid": "text-location"})
33        obj["location"] = location_tag.get_text(strip=True) if location_tag else None
34 
35        # Job advantages
36        advantages_tag = job.find("div", class_="jobMetaDataGroup")
37        obj["advantages"] = advantages_tag.get_text(strip=True) if advantages_tag else None
38 
39        # Job link
40        obj["job_link"] = ("https://www.indeed.com" + title_tag["href"]) if title_tag and title_tag.get("href") else None
41 
42        if(obj["title"] is not None):
43             l.append(obj)
44        obj={}
45 
46       
47 
48else:
49    print(f"Failed with status code: {response.status_code}")
50 
51print(f"\nFound {len(l)} jobs\n")
52print(l)

Now, let’s declare the target URL and make an HTTP connection to that website.

1pip install requests beautifulsoup4 pandas

Now, we have to iterate over each of these li tags and extract all the data one by one using a for loop.

Now, we will run a for loop on this list alllitags.

As you can see in the image above the name of the job is under the a tag. So, we will find this a tag and then extract the text out of it using .text() method of BS4.

The name of the company can be found under the span tag with attribute data-testid and value company-name. Let’s extract this too.

Here we have first found the a tag and then we have used the .find() method to find the span tag inside it. You can check the image above for more clarity.

The location of the job is located inside the div tag with attribute data-testid and value text-location.

The last thing we have to extract is the job details.

This is a list that can be found under the div tag with the class _jobMetaDataGroup_.

In the end, we have pushed the object o inside the list l and made the object o empty so that when the for loop runs again it will be able to store data of the new job.

Let’s print it and see what are the results.

Complete Code

You can make further changes to extract other details as well. You can even change the URL of the page to scrape jobs from the next pages.

But for now, the complete code will look like this.

1import requests
2 
3URL = "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df"
4 
5HEADERS = {
6    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/146.0.0.0 Safari/537.36",
7    "Accept-Language": "en-US,en;q=0.9",
8    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
9}
10 
11response = requests.get(URL, headers=HEADERS, timeout=30)
12 
13if response.status_code == 200:
14    print("Success! Page fetched.")
15else:
16    print(f"Failed with status code: {response.status_code}")

The approach we have used until now is fine, but it will not help you scrape millions of pages from Indeed. In such cases, it is advised to use web scraping APIs like Scrapingdog.

Scrapingdog will handle all the hassle, from rotating proxies to handling headless browsers. It will provide you with a seamless data pipeline without getting blocked.

Using Scrapingdog for scraping Indeed

Scrapingdog provides a dedicated Indeed Scraping API with which you can scrape Indeed at scale. You won’t even have to parse the data because you will already get data in JSON form.

Web Scraping APIs like Scrapingdog are crucial if you are trying to scrape Indeed at scale. The above approach is great but it will be blocked in no time when your IP will be blocked. Scrapingdog will use its own pool of more than 15 million proxies to scrape Indeed. This eliminates the possibility of the scraper getting blocked.

Scrapingdog provides a generous free pack with 1000 credits. You just have to sign up for that.

You can watch this quick video tutorial, wherein I have scrape Indeed using Scrapingdog's API. ⬇️

Once you sign up, you will find an API key on your dashboard. You have to paste that API key in the provided code below.

1import requests
2 
3url = "https://api.scrapingdog.com/scrape"
4params = {
5    "api_key": "your-api-key",
6    "url": "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df",
7    "dynamic": "false"
8}
9 
10response = requests.get(url, params=params)
11if response.status_code == 200:
12    print("Success! Page fetched.")    
13else:
14    print(f"Failed with status code: {response.status_code}")

1import requests
2from bs4 import BeautifulSoup
3 
4url = "https://api.scrapingdog.com/scrape"
5params = {
6    "api_key": "your-api-key",
7    "url": "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df",
8    "dynamic": "false"
9}
10 
11response = requests.get(url, params=params)
12 
13obj={}
14l=[]
15 
16if response.status_code == 200:
17    print("Success! Page fetched.")
18    soup = BeautifulSoup(response.text, "html.parser")
19 
20    job_listings = soup.find_all("li", class_="eu4oa1w0")
21 
22    for job in job_listings:
23        # Job name
24        title_tag = job.find("a", class_="jcs-JobTitle")
25        obj["title"] = title_tag.get_text(strip=True) if title_tag else None
26 
27        # Company name
28        company_tag = job.find("span", attrs={"data-testid": "company-name"})
29        obj["company"] = company_tag.get_text(strip=True) if company_tag else None
30 
31        # Location
32        location_tag = job.find("div", attrs={"data-testid": "text-location"})
33        obj["location"] = location_tag.get_text(strip=True) if location_tag else None
34 
35        # Job advantages
36        advantages_tag = job.find("div", class_="jobMetaDataGroup")
37        obj["advantages"] = advantages_tag.get_text(strip=True) if advantages_tag else None
38 
39        # Job link
40        obj["job_link"] = ("https://www.indeed.com" + title_tag["href"]) if title_tag and title_tag.get("href") else None
41 
42        if(obj["title"] is not None):
43             l.append(obj)
44        obj={}
45 
46       
47 
48else:
49    print(f"Failed with status code: {response.status_code}")
50 
51print(f"\nFound {len(l)} jobs\n")
52print(l)

1[{'title': 'Senior Linux Engineer | VoIP / SIP', 'company': 'Kane Partners LLC', 'location': 'Hybrid work in Newark, NJ 07199', 'advantages': '$120,000 - $130,000 a yearFull-timeProfessional development assistance401(k)Health insuranceRetirement plan401(k) matchingPaid time off', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0Cqv0TaXB1315BlNYUUsQBwFmZaS8YmtZW0EaZAmkSQkJPTHmJ_LttkaDKpRY9-wymGUrQPF8EzE1t4sNHx6IKjqZZjhNxOEepkv_tHIg5iWhBW4hRTHAMVsEjicLPGuKtmdl0TDfCy_yZYQ64S8llZOErUu75ZCtPvd0KjS5tXdhj5tTDGN-r6xh6faqZD15OewLrTq474jda_sCIpgqeuQTQsfQc2Gb57bEtPY8bHRfaTVQwMe2zfnS5F2d5xQPVlnHelz96DvqySwbUpkC4OFyNOIV4thakYwcvRJ8kEzY--vrssqVxz8ZHEF3ZCbD2riD45y2QRJfq0rEB6VOPsWtnzmpNPTwCDnGIglc_bB-B8ncyOjDuwLa7Py-udF7zUcOFMZVujhOBNlLsIYVvJqL0_ZMSsyld0tC1ws-vc8LosIKjj-FN3ApCABkmVl2nnbBswgsigQSx3A8LQiMX9oYmVipWFRE7HcZZmN2Ig1dPsAH5d2-GCX1tUyBZi3c81k0Pph7O8h_gCkfQXTxWu0ax7DdTakrFnP7Ki9acpDh7_IOYo3MdiGKuEdoqeESffd1_r2XzJwDM7N0isQi3UK_DVEVFNmW9WT_XFz5Zfr3gcZo2Ho9TDhd3w87DTRDRHTUDeEoYfMFDwaCtDf48p2o_TFlcN9OIcMbWLZq5Qi00d7afigOKarEI5LYlb6FLqS0rvLG08CovyT5v8Qnia6PpSpJcw5QN_SrlBWuScgw==&xkcb=SoDX6_M3jGpHfWynHB0LbzkdCdPP&camk=UoKtGZLa3XKa1yy8DJXheQ==&p=0&fvj=0&vjs=3'}, {'title': 'FLS Production Engineer – Mechatronics & Test Competency (CWW – N1)', 'company': 'ASML', 'location': 'Wilton, CT 06897', 'advantages': 'Full-timeWeekends as needed', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0A_DDXJpBmDjNhNk0SonxKsswszHAivHatKsF66mYTBcjMCkA3gOusSnWExovNLN1WJA1fSKvovDMAwtqzcDWMiNYNbMHxsNxmQyq_OOmtFS-Tzz2qugnqDCc4AvTFQQEbdYBbftXUr3x4MUu4qEl2Y-yianWgxg2FrjLKGVzvGQ-bSK0OBBz71PoVT7t5XqaHAG1Yw9T-XZg8d9ORbykM4xB946qBg-rPIwQU79nDxqRxTNoIzRbJMQLsx1Ytw4Dautgdgb2dOQLfw_AhSeASnqnbsJBgAUoL-B-HP6MVE6wtnPNn5DR26I26581tUN544NBw8IK1iK5yqHlcrNjLUvjF2pSc2rDGpjtyR1rnFq_00sO4_oKsFhXK6oL2maquJDSDEO14hrm710mQWpLDhrVtso60-3htxanyCrkli-rFKF0Sqr80boLKveI6dpx-iOOgw9R5DJxYrEZ3IE4996PM5XooaNLg-0GR-0BvEDL0gbRdZsZjibE3KiBFz_pBtrpqfss0i9gu3QxOd3ARrhKD8roDarxfLH3mChDc7aVvX1uxH3_ppSYmyJ3QdoyBr0q8C3_4HWJPUTBNp8QF9MEqyBgWz7fB35vB9QlWKp1Vlrb-WJAakFttLEFSGFNY9rME_PzQVEWCpPID48jKRNkO_9YbdrIIHFM_BWXTPWMnwl2E2nEgQwSjAM1O_SSK2yC_0H0PIiXSYMTscMRXY-4hxOcI38zqWYMx7EZ8p1QZ-Ckqv18CZJSoux4hqaJq--AtrPTbETeovrrXc58X_f3wLE2kxZI8w5UG8j95mVKgoHtyVV346&xkcb=SoBj6_M3jGpHfWynHB0KbzkdCdPP&camk=C3EPSzFlQw-snIlGi5a3BQ==&p=1&fvj=0&vjs=3'}, {'title': 'Software Developer - AI Trainer', 'company': 'DataAnnotation', 'location': 'Remote in Greenburgh, NY', 'advantages': '$50 - $100 an hourContractHourly payFlexible schedule', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0CnM4TERr6XuVBW3VTRACvDXDx9S3Pbn6a0SwhkmoLTiIZpxuUfjFtTfCfdb-RN_CGGC1IhyZUV0hoNN4rX6Y4lFaVMX6cMg5ugh3ASNkxiCHHZs1VTn4S-KaJwvFLxeuDCsUJ3J0FGBvZkSK41SumjOvBfw7O5h2-wJj7d3jGgZo3ddmWviWw9FWMHszZsp5MLS5k3DGCoIoIQ5CZ_Dp7Hw3xztOXXpHFEgut4odg5_Gh2YIdSEZvZCd8x2tIqs-0ih4tVbH7AABB0C_4_ehNKKP-aVLPrfMMsgFB_nZ6kJyt9Guy3XwQHy0IT_fpPK9rJBQzR9NBp1X-5U4qcCuf4QCJnamRlnTDSfmVX1A8_fA282JxqoeTlk3xQbuog_SHrlAYovjKf1OF0EOes94EBNBSaJLZmsYwO_nZxxsri2lVWMsDZdZkMyyPrpXvoitulrcaCq8cgctwe18_lgvdqOLKC9UimUx_Om-wb_G-ByIu9kGIuqL12Hk5VPggyHePmIkgM0P8Cz6q1vrdHXibD0wp8401v9pMyt--eDmbQboOUbTvjhUwFwUJOeSq62yvyLPzBqAyjW7Ywt3XQc54ob1Zs_vpPqHdbdbq4673UGgoacVayeS-jR8c3rTqMwY3jhdIWIx33JniXfVZ9c-m8j90t5ZF8OhhrOq0KFe-iCh8S6sdlBYaT0HhuwuUSb8MGSXtTaoxpKY6EB_D2kggTQF-hVyfBi0V0dSHDyZrJy6HwSvjC-DgWzFsTaSvN7KE4EY3td-mRKfWA_o_6RRl28WmhvW-nag5YMEHmMl8Jk8dxKj1zqPoQNC4fFFNL3urZO0V-Q3WKILdX5FA3BZ_rkZkY593ba9LXXjoDtILm513E3KFc7kvVlYog12kBaaA6mpJiwAC_gw==&xkcb=SoD-6_M3jGpHfWynHB0JbzkdCdPP&camk=UoKtGZLa3XJ4m_FGbRlp_w==&p=2&fvj=0&vjs=3'}, {'title': 'Back End Developer - AI Trainer', 'company': 'DataAnnotation', 'location': 'Remote in Newark, NJ', 'advantages': '$50 - $100 an hourContractHourly payFlexible schedule', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0CnM4TERr6XuVBW3VTRACvDXDx9S3Pbn6a0SwhkmoLTiIZpxuUfjFtTfCfdb-RN_CHnoIGagZvIJlTzwBE5mC7MR0DHhraPw5GaDzfk-oHfwlwq6it3Sf6cFrb3jtSsUDUcEm5Gfy738GqlrEGql6zR7_J21-j6OeVP5iRZ8YnwdQSfapI-41DqXJdfJaFDKVSnencPuvhp6erZYImT8muI9USrW8tDLKA6qTNv9MNxmkOmy8g1M1Va4doMAm7W0Mq3S4FF01sDp5vEjQqBFXbA4xMH1QE-u_rgYpUIkqC3QAIodZ7u1qY2S2IjdubZWEeCflnKV8xYGCP3mnAbHbg0vPytTLoPhA3qlnToL5J_lIs_sDUxt7MiizEBQATluBWMtJr525vw45cjTcS37L7I6vSB4w9xW5E1BSuNK8dWmZYQVcbKJTVdCShlkjc6ei8bbOk7LhafOOdMIggAPWW_A_qFyQn3zsn8DNSuVYaeW-fwzlulRnd-JV9Dqo8jP1cKF-l95f3EFOC2LaV-qEDgKrekkoD_OhXs5t199_GWWG7TCT-1fLh6weggFhx8xp_Xd1EHOp4-fAmk19NqMk-P-PBgKYEz3Bw60Q9KKdU2K6LCHJya8V5CHtq3E2IPDWyWVyHsk-OUYRX9x3AXJRcB0EC23c2PRyirKu0kuvR6fJhVPLTCtZ_W7tDjHEkz6VT9bAYsqzPI49a6oEc5pHGq2iY9OsFq46R-Bxf39MOcH-kb_zdFaZQL7VjKd8OVAVQD73yzrIQiVaVVrTYtKSoWTDLSOFsETOh0-iIxDTiSrca-M1VtVjv1OcSoi7tIeLboGReuACMtZNilv8tOn2_X22UduFIzkJThQ_3B-Q5pEKlhDMewOdPUeKnL5pbXi_0=&xkcb=SoBK6_M3jGpHfWynHB0IbzkdCdPP&camk=UoKtGZLa3XJ4m_FGbRlp_w==&p=3&fvj=0&vjs=3'}, {'title': 'Back End Developer - AI Trainer', 'company': 'DataAnnotation', 'location': 'Remote in Newark, NJ', 'advantages': '$50 - $100 an hourContractHourly payFlexible schedule', 'job_link': 'https://www.indeed.com/viewjob?jk=fedcba9876543210'}, {'title': 'L3 Network Engineer - Cisco Fortinet Meraki', 'company': 'HTC Global', 'location': 'Brooklyn, NY 11212(Brownsville area)', 'advantages': '$106,063.70 - $120,000.00 a yearFull-timeReferral programProfessional development assistance401(k)Health insuranceRetirement plan401(k) matching', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0CByRoXwCXtj1i2vGe-X-G__FLG5LciqypPGT-dmPwjVbxiHr05jDoKfU8-nox1nXeTDKomI_HsoLlMMgWSFL8VRKko1eNkcChgf_Rv5F9KOYaZpiy1Zkys9f48oe5OTDleVn5_TnysyBnwUQF2IHxwCDtzzk6qXwBV1y5bysp_XZNRg5JDdjcBoy3PdUKH_PBOgXDbrr7U08j7HrQQuThCL5APxOiBXN7m5CurZ66joQ-oW0f3pXP70C936TepwsdY5MQgM_gQRGa8b1H1GVIn57epxftBWeSvy0ZIQUb_pGdjEPgWtRPnG23My-3S0IVZCjiLzveuTEe-i8K52NBVwy0gkhRA2LalH9YsuKVv6eP38mqjkdtYg_8Jsz61VyuXMYO_ko96MV9Rb0mCyIeYZhZZleMTGWcLipKc4ODnRrPBs6jCvg3uUFrh9NvV99jrMNeC8xsAAp0ofEINLSCTDnFAw7bq9BZ2_N1lkDInW9o9a3c26C-prvn3KTy5d0EGzHMcQHpexNPdpFmt_Q4LGNmpDTgJkJlJMF0i-PcJkocVMDXBKVQgOuCkfMPs1xC5XeoIbTOeSgkT3zcaQckzHvNWD2RyR3oXElPIUH4pFO19A_ab-Ehfw7dHXV-kaV4hyrSP7pDCRfTFvP1Qp2C7JDHFuHKhVWRIPr__djdDVLXd1_RLVI7BtfGNZa8wz1gJVF8Swodqo5lpRPQdL0fp0Z9JoUcvKtI=&xkcb=SoDE6_M3jGpHfWynHB0PbzkdCdPP&camk=C3EPSzFlQw9KZa2kGuPr2w==&p=4&fvj=0&vjs=3'}, {'title': 'Senior Data Scientist', 'company': None, 'location': None, 'advantages': None, 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0BKHxJ4d4pYnhuHc82NW3TJfQdpwXQLk8tpvD9qPefWvawd_FfSV1eommUtV_5nhxt-tLDb7ioDu5L3x1AcgD-N1BZNKwstpIYJKSPm9ZNFieRiwV9OoxvN1aJ8-cSsagwM-ZtBnEaZe1TfKB2DshIDj1v9ahOchbq33aURQ-4aWhNXL0lZsJoZGCSatP_zhKtuC539n7_Wws-NNLzojPIEnAq0JLAdSF9XGsr2U17UoFzPBrq3RrYjEiQahZ1Hi1RwPaou279gvwZdSsGVkLpCQsDAI6h38p9QSndaWTSqeyvSdrZ3xDCfLlN_yVhwyL_38m8FJ0k5mwxlQ-qVoNT3XOuCpg-Ph1WioN0AvaYoPwAst-86obJsnhO_vRtXp8KNtXbHZcw3cdkf5Io1axg1VQqI-gzEEt32Bhbo795TButnsgVCD_e9asSQ95iKKOcukPuy89UGY6zN-ZrwbY9zhOY8wydISW286MwgtqlZdzJ9TTSv_r3qFCaDek5QOk3PbKEz6PWGxCQmvY2tM0onp2_MIxFBluhflKKw6RVKp6HgvgW7cS7-gO3U03Aytpfiwr_R0y2Q4VKel6pww-TWrLunIFSDYQDjn700orsXkmS_FXHulFsatEhTAJzsFPPbaUseClk0rilN2YyMNyYt6PrJVOcmot5e8HNFv2pI4y-gDXB1dPshrKb1eN4kVmdJLUvmoyVmkpAJzcmyGIz-C_3RljykpydQW_KjxnLcVeDWC22mdUqBARRGAeocFFbg6cCdTFH3CcBWY62oVKt7Z2Hc3OSQXObsMkDFtzKMZcMdEXpR7kAu&xkcb=SoBw6_M3jGpHfWynHB0ObzkdCdPP&camk=CPChkbYSGj-gB5BRehoS5Q==&p=5&fvj=0&vjs=3'}, {'title': 'Senior Data Scientist', 'company': None, 'location': None, 'advantages': None, 'job_link': 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{'title': 'Telecomm Expense Data Analyst', 'company': None, 'location': None, 'advantages': None, 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0DAOc8tLk0mIom9N5jmJmlgqF5LKcwOQU9jeBmjvGjWXS2E9rhJMa_Z7ipVqzHklT4bmJgfpE7KUP0aUr1szIaB03hjNfUTr4I7u5a7PkocqO9GhkrwWGlf_m3W7eLsuBruA8EN6xQX_GK2Ve7dVIhhnmkFw8oqZghNtv1sI6-DDOoph7t-7p_ECltIF12RdAvJR_Goug0SsJof-8VSHLwSyiZZtmxMujq-QJo8-BDacHwO7MvC2jXufDoU8Z_ZHfj7sR-iDekH4tFcmWu1m5ukLHVmWm3vgHmpylvsf5FHl1sjqdp7gFLXbaatmn8RZIsoPmuOVtnRBjMf5G1D8jDRPO2IKVrDruXBC0ddDWyZ4CtB_5HTWnJdlIguoVYt7tpp4Q6xisg1hS_oGK7sbNfQnysUMVP-l7RErKjyOCQ_u1jZSAt1F0SXI-VHJswpz2kxiOzPpSc6tnuYtGamkJMiZX1UsMMqtrhtA4KHGqhLNczh3XBpa86lceTdh1S4bYAFUDyTT7iaU8qgu7HIhtIdb3JP6DM5OR8I2FQd9hqn8kgaSu8_O2KvQk87zZyBQ6cFO8G4-vGJlVnEkxUl8QV2xVzGPp04XvQjAI28wrOGev0FgsGtKgRJNayuwUNRad6GLNM3SHVrCR5AMnSXwFWSRgaTS-Td2YocpFmpFwnw9Rvuhv_5UPxWR2wkN2BxduCyEzfoqJEjeg==&xkcb=SoA-6_M3jGpHfWynHB0EbzkdCdPP&camk=4HOcmqOLYrAEYHsf8Ryyew==&p=15&fvj=0&vjs=3'}, {'title': 'Telecomm Expense Data 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{'title': 'Advanced Processing Cleaning & Contamination Engineer', 'company': 'ASML', 'location': 'Wilton, CT 06897', 'advantages': 'Full-time', 'job_link': 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'GREATER NEW YORK MUTUAL INSURANCE COMPANY', 'location': 'Manhattan, NY 10118(Chelsea area)', 'advantages': '$69,100 - $124,400 a yearPerformance bonusYearly bonus', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0DDgIJvRb_i-6btgJIwvY_wUKeyiL1Jk4MjP-_nznPMfBdluFJIYQ3yi8Imm_Sr0FkpZbdeZKkwgASYhgfy5gO0g3HhP4VUfjE0joLb7omcS4wCrz0_Fov6REx90p1jc3Pj-0stNT_vxUZkcSdSsYVfX1TQB6zxCXvVBGwPWj4NkAyC3swtjrtx6PvEuBUXem53pFYpRdiBTEC6rV8ejXecMw5KZSeFKzBuvtwarY_tiuKi5YBZAO33Jd74h3pvnfyRAOi3sjZzhgxQVz5bed7nLxmk9gttCebLEjqM-YfsN0hUxm6m5fsBXKRJPqj4V5ZxKm0KkPKhdTljCQrdhSE39W0G3Ir_499w44e0lfOIQG4l4nEWGDz2WlEZz6-8mLt2FIoUYMGVOdLDcIVZFvPMq032W9FcalEfJo6gwTyUpE-hZJOabAJG64RQ-SeGTHwq42AVax8csIB7rpI8QiyYX70GagQNlwhgCKel21OPIZSgu4oydmCtyvv7kvLz2JA4LW0KurAY62NbldwabxCvna3-6aNM6JQvpfreI6dbliDf1r6LHVpP2CqwmwU3-IWzP3YuN_Y0LSxxKqEq8s2sefcJATDOCFjGrZKog0Jgl1IPwufFLABeEV7FJ-yoybVI9D9cbeX7PUKFXidy7HMResVRiCQ-ZHmYMzQSlWXanL7oR_OLl_tvk0jnFtUAvAf08zs6_Af5MQRRxHSJZlxJ_xs-cOIfs5mqAFABgIdhd4vNFeUEMf_4Thjt5J9--kp5wRKKJdyDkw==&xkcb=SoAj6_M3jGpHfWynHB0dbzkdCdPP&camk=nUmJqO2E8rhHIw5UKjPYDQ==&p=22&fvj=0&vjs=3'}, {'title': 'Project Manager', 'company': 'New York State Energy Research', 'location': 'New York, NY 10018(Garment District area)', 'advantages': '$78,333 - $131,298 a yearFull-time', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0CSYEVr3Q1_qq8SUkv8YciDzQ2FnV0lOgsVqqV_qYyQht1AF_V_yRBI-VikX3z7e0TrbJnkn9mgUTpmcol7fdv2MopyFQx9W4bqDuI-B_4dZ9Hdfzo9PRq3z5eIhgWKq-podREjyAYAZkxz925Zr2dAq4D9bMmzjTwSYDwSJ_b-LD-bScMPL8d99K2NNV3940wQYlSmgcckQSVwR4LnN-FbE_5-xHH0EbOGtifyeQ7MH8hZump7zhpxM_OvoItyj5itIcbezs-RiQmJN1uNBxfM1HLCGujSuRMInSAecj3wk7TN1GdaoPISod4yVYZMkgKIYyVS32tc0XB0nFdZ9u6Z3WZim_PrUiaSu86_i-9o1ROkND49vwu40naiqbOgbaMiu08VoT8IBIBeFXCUYuz8FqaYLBTXvvcSXRN6qK8T_UWCr0RrspYxENfSypjXIfEz8dZN8OYmzMoYCPRp5xaH7zyuXM6qw5OMmDE67XRMsKcALEH5SU9nl_e1vI5Avwane7fmln7VX6Tn664h5-b0w5KoSKq7y9OHxsd-Q1l_XCbtriXsRAws35qT2JqsG84h0_Uxco6ouaAYFd3Ka6VG0Nv4BjqS-AdUamMM6xgkgrCc2LxO8he8qIcNXMBP0Ax_T5ynJgp1pxFzg8K1Y857HYm2Rj31SJyFhd2Wzd6joizpsalwSpy1Qz1w5soVHO3QQcrP7gU2hcV7wX3SwpU6l6R9bWyNgP9s73WKTmkoIbclaaiWYG8ZFgkWEgzp-gJAbqtxO2A29i22MPwSnDzxLJ31yl_YnrKgnp2Jn9teRCCfrm11f-jWQT6XP7hJWCCb0KGEDN35VF7fsoU9kPcp9dzz83QiWpTwzmqrZdlCpt7O3td2NCXK6OtELE0IhMrlqkDsJHkszjTO2y3pdxhj&xkcb=SoCX6_M3jGpHfWynHB0cbzkdCdPP&camk=ethIe0s0heeDf_isdJfbEQ==&p=23&fvj=0&vjs=3'}]

1import requests
2from bs4 import BeautifulSoup
3 
4url = "https://api.scrapingdog.com/scrape"
5params = {
6    "api_key": "your-api-key",
7    "url": "https://www.indeed.com/viewjob?jk=1498699c37f1418e&tk=1jo435dgmjo21800&from=serp&vjs=3",
8    "dynamic": "false"
9}
10 
11response = requests.get(url, params=params)
12obj={}
13 
14if response.status_code == 200:
15    print("Success! Page fetched.")
16    soup = BeautifulSoup(response.text, "html.parser")
17 
18    # Job name → h1 tag
19    title_tag = soup.find("h1")
20    obj["title"] = title_tag.get_text(strip=True) if title_tag else None
21 
22    # Company name → div with data-company-name="true"
23    company_tag = soup.find("div", attrs={"data-company-name": "true"})
24    obj["company"] = company_tag.get_text(strip=True) if company_tag else None
25 
26    # Location → div with data-testid="inlineHeader-companyLocation"
27    location_tag = soup.find("div", attrs={"data-testid": "inlineHeader-companyLocation"})
28    obj["location"] = location_tag.get_text(strip=True) if location_tag else None
29 
30    # Job type → div with class "css-1fajx0z"
31    job_type_tag = soup.find("div", class_="css-1fajx0z")
32    obj["job_type"] = job_type_tag.get_text(strip=True) if job_type_tag else None
33 
34    # Salary → div with id="salaryInfoAndJobType"
35    salary_tag = soup.find("div", id="salaryInfoAndJobType")
36    obj["salary"] = salary_tag.get_text(strip=True) if salary_tag else None
37 
38    # Skills and Education → both share class "js-match-insights-provider-u74ql7"
39    # Skills group has aria-label="Skills", Education group has aria-label="Education"
40    skills_group = soup.find("div", attrs={"role": "group", "aria-label": "Skills"})
41    obj["skills"] = skills_group.get_text(strip=True) if skills_group else None
42 
43    education_group = soup.find("div", attrs={"role": "group", "aria-label": "Education"})
44    obj["education"] = education_group.get_text(strip=True) if education_group else None
45 
46    print(obj)
47 
48else:
49    print(f"Failed with status code: {response.status_code}")

1{'title': 'Kafka Infrastructure Engineer - 6205104', 'company': 'Accenture', 'location': 'New York, NY', 'job_type': 'Hybrid work', 'salary': '$50 - $60 an hour-Full-time', 'skills': 'SkillsDo you have experience inTerraform?YesNo', 'education': 'EducationDo you have aHigh school diploma or GED?YesNo'}

You have to send a GET request to https://api.scrapingdog.com/indeed with your API key and the target Indeed URL.

With this script, you will be able to scrape Indeed with a lightning-fast speed that too without getting blocked.

Key Takeaways:

Indeed job listings can be scraped using Python libraries like requests and BeautifulSoup.
Important fields include job title, company name, location, salary, and job summary.
Pagination handling is required to collect multiple pages of job results.
Indeed uses anti-bot measures, so scraping at scale may require proxies and proper headers.
Structured output (JSON/CSV) makes scraped job data ready for job boards or analytics use cases.

Conclusion

In this tutorial, we were able to scrape Indeed job postings with Requests and BS4. Of course, you can modify the code a little to extract other details as well.

I have scraped Glassdoor job listings using Python, LinkedIn Jobs & Google Jobs using python, do check them out as well! Recently, I also made a tutorial on building a job board from web scraping, this blog gives a step-by-step approach to how you can create your job board with actionable insights.

You can change the page URL to scrape jobs from the next page. You have to find the change that happens to the URL once you change the page by clicking the number from the bottom of the page. For scraping millions of such postings you can always use Scrapingdog.

I hope you like this little tutorial and if you do then please do not forget to share it with your friends and on your social media.

Additional Resources

5 Best Indeed Scrapers To Test Out in 2026

Web Scraping Indeed Jobs using Python