Indeed is one of the largest job listing platforms with over 500 million monthly visitors. Whether you’re building a job aggregator, tracking hiring trends, or training an ML model on job market data, scraping Indeed with Python is the fastest way to get that data at scale.
In this tutorial, we’ll scrape Indeed job listings using Python, handle pagination, extract full job details, and show you how to do it at scale without getting blocked.
I’ve also written detailed guides on scraping Glassdoor, scraping Google Jobs, and scraping LinkedIn jobs using Python; feel free to check them out.
Let’s get started.
TL;DR
If you are in a hurry and already understand the logic behind scraping Indeed, then you can directly use this code.
import requests
from bs4 import BeautifulSoup
url = "https://api.scrapingdog.com/scrape"
params = {
"api_key": "your-api-key",
"url": "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df",
"dynamic": "false"
}
response = requests.get(url, params=params)
obj={}
l=[]
if response.status_code == 200:
print("Success! Page fetched.")
soup = BeautifulSoup(response.text, "html.parser")
job_listings = soup.find_all("li", class_="eu4oa1w0")
for job in job_listings:
# Job name
title_tag = job.find("a", class_="jcs-JobTitle")
obj["title"] = title_tag.get_text(strip=True) if title_tag else None
# Company name
company_tag = job.find("span", attrs={"data-testid": "company-name"})
obj["company"] = company_tag.get_text(strip=True) if company_tag else None
# Location
location_tag = job.find("div", attrs={"data-testid": "text-location"})
obj["location"] = location_tag.get_text(strip=True) if location_tag else None
# Job advantages
advantages_tag = job.find("div", class_="jobMetaDataGroup")
obj["advantages"] = advantages_tag.get_text(strip=True) if advantages_tag else None
# Job link
obj["job_link"] = ("https://www.indeed.com" + title_tag["href"]) if title_tag and title_tag.get("href") else None
if(obj["title"] is not None):
l.append(obj)
obj={}
else:
print(f"Failed with status code: {response.status_code}")
print(f"\nFound {len(l)} jobs\n")
print(l)
Make sure you are using your own Scrapingdog API key.
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
Before we start with the tutorial, make sure you have installed Python 3.x on your machine.
For this tutorial, we will scrape two pages. One will be the job search page, and the other will be the job overview page.
We would need three libraries that will help us extract the data. We can install them using pip.
Requests — Using this library, we are going to make a GET request to the target URL.BeautifulSoup — Using this library, we can parse HTML and extract all the crucial data that we need from the page. It is also known as BS4.Pandas– We will use this to store all the parsed data in the CSV file.
pip install requests beautifulsoup4 pandas
The final step is to sign up for the Scrapingdog trial pack. We’ll use this web scraping API to extract data from Indeed pages.
Scraping Job Search Page from Indeed
Before we write our script, we will first find the location of the data points we wish to scrape.
We are going to scrape all the highlighted parts in the above image.
- Name of the job
- Name of the company
- Location
- Job advantages
- Job Link
Now, let’s find the location of each element in the DOM.
The job name is wrapped inside a tag with class jcs-JobTitle.
The company name is wrapped inside a span tag with the attribute data-testid and its value as company-name
The job location is wrapped inside a div tag with the attribute data-testid and its value as text-location.
Job advantages are wrapped inside a div tag with class jobMetaDataGroup.
And all these jobs are inside a li tag with class eu4oa1w0.
Scraping Indeed job search page with Python
import requests
URL = "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df"
HEADERS = {
"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",
"Accept-Language": "en-US,en;q=0.9",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
}
response = requests.get(URL, headers=HEADERS, timeout=30)
if response.status_code == 200:
print("Success! Page fetched.")
else:
print(f"Failed with status code: {response.status_code}")
This Python script is simple and easy to follow. Now, let’s see what output it produces when we run it.
You got blocked from the page. Indeed is protected by a captcha, and it can only be bypassed by a web scraping API like Scrapingdog. Let’s now try the scraping of the same page with Scrapingdog.
import requests
url = "https://api.scrapingdog.com/scrape"
params = {
"api_key": "your-api-key",
"url": "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df",
"dynamic": "false"
}
response = requests.get(url, params=params)
if response.status_code == 200:
print("Success! Page fetched.")
else:
print(f"Failed with status code: {response.status_code}")
We received a 200 status code, which means the page was successfully crawled. Now, let’s use bs4 to parse the page.
Parsing the data with BeautifulSoup
import requests
from bs4 import BeautifulSoup
url = "https://api.scrapingdog.com/scrape"
params = {
"api_key": "your-api-key",
"url": "https://www.indeed.com/jobs?q=python&l=New+York%2C+NY&vjk=8bf2e735050604df",
"dynamic": "false"
}
response = requests.get(url, params=params)
obj={}
l=[]
if response.status_code == 200:
print("Success! Page fetched.")
soup = BeautifulSoup(response.text, "html.parser")
job_listings = soup.find_all("li", class_="eu4oa1w0")
for job in job_listings:
# Job name
title_tag = job.find("a", class_="jcs-JobTitle")
obj["title"] = title_tag.get_text(strip=True) if title_tag else None
# Company name
company_tag = job.find("span", attrs={"data-testid": "company-name"})
obj["company"] = company_tag.get_text(strip=True) if company_tag else None
# Location
location_tag = job.find("div", attrs={"data-testid": "text-location"})
obj["location"] = location_tag.get_text(strip=True) if location_tag else None
# Job advantages
advantages_tag = job.find("div", class_="jobMetaDataGroup")
obj["advantages"] = advantages_tag.get_text(strip=True) if advantages_tag else None
# Job link
obj["job_link"] = ("https://www.indeed.com" + title_tag["href"]) if title_tag and title_tag.get("href") else None
if(obj["title"] is not None):
l.append(obj)
obj={}
else:
print(f"Failed with status code: {response.status_code}")
print(f"\nFound {len(l)} jobs\n")
print(l)
soup.find_all(“li”, class_=”eu4oa1w0″) — finds all job cards on the page.job.find(“a”, class_=”jcs-JobTitle”) — finds the job title inside each card.job.find(“span”, attrs={“data-testid”: “company-name”}) — finds the company name using thedata-testidattribute.job.find(“div”, attrs={“data-testid”: “text-location”}) — finds the job location the same way.job.find(“div”, class_=”jobMetaDataGroup”) — finds the job advantages/perks section.“https://www.indeed.com” + title_tag[“href”] — builds the full job URL by prepending the base domain to the relative link from the title tag..get_text(strip=True) — extracts clean text from each tag, stripping extra whitespace.
Once you run this code, you will get a full JSON of all the jobs available on this page.
[{'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': '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': 'Machine Learning Delivery Lead Research Platforms', 'company': None, 'location': None, 'advantages': None, 'job_link': 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an hourContractHourly payFlexible schedule', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0CnM4TERr6XuVBW3VTRACvDXDx9S3Pbn6a0SwhkmoLTiIZpxuUfjFtTfCfdb-RN_CGGC1IhyZUV0rDMfGDcHAQl44uBebhYWotEyzuxB3WVc6X_y0cLB1OiQLGiMUNQTegOW3qSh_mmzCOC3dtdpIZI0059_pQEyp3yUs1TNJg8h4LQJfMBPSNKTG-oPYutlzQF6-cij9JmxOphaT4t21TLaXOze4f93l9yIPzW3hy7eVVwr-er1_UM8ek883N3KB_LIHIenu_VTFoQRvt1Yspg5-BXKr-1t-dgA_OgclJu54P-vWtEcABqwP-svA1Z3iXTOlxlBZ_nkj9Mrcde7mfF0qqF0cuo7M3uiWpu_TrE5pFX5mZ8tQqH2NkPkmX9En5I7CrzeFsUPi-QH8tLgM0LALhorCeJnYwQgq0wHypuLDYAYjOJKFkkS9bUKfclfNVzcFM0mTZc74i94oChpZOd4Z3-QD04GgyuiBq_hnxbKVc984l5jsTCJ-Qg2pLCgsERQBPMjX5bBiwugXrIoOqpAgvbcpeI33n9RgGhlWszzGwIja6aQxFkSX4FcGFCYpg2avnZopWj7oMMSR65Wz3RMH_XvEl6ao01ljyZxrdopJ6cnxDiuthk8y2K2NeGDuIajChPy1t-kC2Ab1LJnb1PNKLaqLZ3J7Gv-2ETSx_zWAPBJAaNtHX4hWx6HDFV1GATLkMtcnVLdxGCT2EWdfHgKEs94_a3IiEN0WYRfV7bv3tvCkm-rxubwZhIH_Cz56VSJIYKLct60PMMh1qISnEDyafZqs1w4RTWT3Ap4DrtvYGZmjhLPBm1gDUCM9Nuqhk0wwbfEpSBivgH_5bClje_a74tWlamilxUQYzXT0ukCcEaww5XhNau3yHNX6kDaLwd08Zmg76paQ==&xkcb=SoCK6_M3jGpHfWynHB0FbzkdCdPP&camk=UoKtGZLa3XJ4m_FGbRlp_w==&p=14&fvj=0&vjs=3'}, {'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 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': 'Business Intelligence Analyst', 'company': None, 'location': None, 'advantages': None, 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0AgUkyspmzRSIpH6dtwCluFSDzADG3KKzFYOWcoWI1J6jwCU-05M_iXrUo-8KHaUnCEOeDDQfplyjyePGVOYMUi6cIMd2LYX4ZXEbiEMVcTYicIGBfCH1ZW3sPUxW-9iM5WOAFSjL-z4ehT2tDrN4OZiSqGFh4XCdSQK3ADywOAejjILgBux_KYNNCivB92z-T4E9_gcGUr75QOrFHUt99XAOaOK0FZe051cQ0eMqhZljoi4hrZYgfSKcdR1EDR8_k2RmJJCsLk4HR-aW0cZ4alqprseZstfrrn86cxr6YtSLVDmGnG2GcokYeSVL3yymsvo85AqXJMWKwcypNtKvJZn38oDtJbG3jNC_-1vDXMrIzkz9KMQwZJ2FfjFG3ZbhPZrNthIpbxaCxNW3bIA4x_fWskT2O6LaQq-IuBeWKa7ToZTZ46zP5Y8zrW8lpAgBuqjbMb40AHJdByrDLcpl0Ai4hx6z8F5vbuu0qplmNLWFfyFp0VtUqavSYtI2TeSekPwWk0op2ptTTuCwDNTwzaoUdQsPvFhv_9IeCd7QR3vZjWn_8DEeDod0xE-1Iol_Nofz-kyMt322lIzbVI2vKsNk5ibY1CK-VSjetmKFxshGix7GUKUJtSugBthRXkBoASWlVBxMh1BoACF-RrvUmAKFtgMAq6ds_d4zn7IAi_wQ==&xkcb=SoAZ6_M3jGpHfWynHB0bbzkdCdPP&camk=nUmJqO2E8rhF7T8ypqczEw==&p=16&fvj=0&vjs=3'}, {'title': 'Entry-Level Data & AI Analyst', 'company': None, 'location': None, 'advantages': None, 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0AZV13m_7z1wrfcApyIvwEj028vc2S5DqnhYE3oYFd_qtHAbQVe35ISAQiJJeWHydcL0VQJiNnjJwuN9y__Eb-knmbIEBg9yWy_A_uF9FO_kSGj4NLmpayYJIru5t3S0Y8BMvUzUBWYWU9nsRkfvJKzxpFH-dzGmZF11AhxvMhy7Ir5J-5hO9krEHUM_tGWTY3qCkW7WqCtlCicmFkfy9Di2UQ5undUrQ_ajrLb3Jk-CZTbmsBzC-tFja9k4iyUlaUrdcRMgshs57khvs61XyNDI_Sf5_L9qKSvX1z2ZL99wAGqz88JBGa-DKjpuM5jpWEKluqc5f9inqJr2gkhJlGLYrquFhF4PsMR4H0s26h1pppBCKBzxSwLA9E8Xw1n5vN71HqtDwN2vHsObYrLZXhQk1LNI_urW5kU73x7WxR9jxPhakOeU-4ZyvwRkFZVckCAulP_2V_aM3o7cDrx6jMOdHUd1LR-qHNs2W_nI4aNc1M2WKBYAfPPygIZry-Uxg2NWui5bwCBax3Tog_VGkjfo3HbFj99e2yWPD7PfdcbR44SZzTsp7KRCpldqtcT4ENzsd1eUApfidOdUImEdKnU8vj4BDmHYHs-YfHAJRkqazcntuDs7V0ZR3lNLU7Wvwj61cCOP1ikYd-Ilxk7ZzpMG0ABnjDa5nDKJpQk7x4MSd3633AX21GppoKBNj6wuQBqcYJrME1sew==&xkcb=SoCt6_M3jGpHfWynHB0abzkdCdPP&camk=C3EPSzFlQw90SlzXVuntTg==&p=17&fvj=0&vjs=3'}, {'title': 'FLS Production Engineer - Optical Alignment & Metrology Competency (CWW – D2)', 'company': 'ASML', 'location': 'Wilton, CT 06897', 'advantages': 'Full-timeWeekends as needed', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0A_DDXJpBmDjNhNk0SonxKsswszHAivHatKsF66mYTBcjMCkA3gOusSnWExovNLN1WQVa8iL_W3TWm6iZAbuyIuPBPi1TVSwAFxl_PRXSd_Xm0ZZhEQZ_vqx_ZTWBqrAbKmnYyZhvQvUM2PNmhFydnhPV9RVy_XZxa8d6iW02M4_Bd6lnY19e-mUf9BigZMoGPiDJtL0gsCP_8KMaZLR38EdRd1kn9Aefuhf07EG1TinYwpb3dV3IudBeV845En1u1DtU-J6iVoFcZxm3A-k7TjwZmKkjS61ep1SSLLH866Tr-wp_B98UQKt5U6BkZVvq9ZOM2BVgax4vzYaD-QDKSKbpAyG1N4ZtyARjZh9b2TnMoUl23059N_1lng3AJB1yPpT_lShdgXmBo_L53njqYIXOIHLLMkUp5Az6EaM0cpNMPwhKmE96HTKKliCvhMjDN-RFNRo8z7G2SU5KRd3vATBdD5Qsc4Oep53fF_GFmJPm5CaNDqMsb5rjOToQlTbZiueMETECpsHZA7ZbBPmX4OE8YmJQg_Cm13oYr_JwR5kP6KwDwrjDXA5nimYC6P4wUVrCKBp42Vt4QPEKW-_gqBiDnnuD93S6gE0Wx3yxINTl2Lg5miO_AsY8KOJkFkEwWvXcc205WwQFCGfFKbb5hcv9b7jCqXyYVswA_vFkAt7_K6Vd-nf8v4lLV_30GOmRYJGA3s-rFujw1_WElB7Bxl_Tc70agX7rHV_LKblh12JwOhGx4Pao0Ze4F_TWsGedSaxAsmWX9z_ZiHuS48JosJpvkV4gY8RFXNgolkPv0rlKg6BCgUF3mjszon5Oh2xz8=&xkcb=SoAw6_M3jGpHfWynHB0ZbzkdCdPP&camk=C3EPSzFlQw-snIlGi5a3BQ==&p=18&fvj=0&vjs=3'}, {'title': 'Software Engineer Analyst - AI Trainer', 'company': 'DataAnnotation', 'location': 'Remote in Hoboken, NJ', 'advantages': '$50 - $100 an hourContractHourly payFlexible schedule', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0CnM4TERr6XuVBW3VTRACvDXDx9S3Pbn6a0SwhkmoLTiIZpxuUfjFtTfCfdb-RN_CGGC1IhyZUV0ipydPlTxvi5gpxmMwBTRX8STKoF64Ulf5oxLXAsEb2biyUS5BoQ03o42C1jldr6QXHY4XqzdMKH1PWR6YAVHjUAClqklkO6ulbpW_kycOzcSVVEUbJHt2rpFQT9UY7l7tPd2urT0TW3TGfgmzJXXUSZqMOGZbyY5_1ky6TMZNdAgulSxqeOaxBEWyCpjIQNTfmI7dXmAALtGiaZqesID_tFP1ro21fAD6FjLAD6n-Rj1Tb6QFCvC-BjO9lXmvuXnh0iw8H5uaRhMJdlj6drwdX0pNMBerV0YP6YB8_QGMp2a6Noaga8gnZZFF0pB0fUMwcr1Sj8Esr-kFI1n0MwUizbSNAwySonoNHjB9HT-lRPDLA4O_Ui9QbAM9_hh1aCyhpNcuENDuJph_cVbW1r3VZZVbmWwNl_Y7tAQ2MmUkKyO6hJiL7PAsBLLYGBt51H0--wW1YIZM9xBqRN9wvuR9RkwZn0K1wjszpcBQEkOaoXzly9U-9QEiEXKaMLV6MpwFd6mxG0oQjDQHDLmreGSwX1wldqrv3t8Za7IKB4tcQ5A-aUWZvu10tIuL2H5ahgFUqK5SBE9XNqf2IdUpD3rz7-Whq25guaoS8bsETmHEWDTJkrtpMGEioVN0U1m-akHgkjW5YKx3z4A-Y3CbcD-ZNuLOEFPBuxzFEatKZvfPCbIV29WMpVTSgVftvKQGKJeqcRfU2Hu0BE0OBLQqWINwU971gn7dexDnaXJtFGJEW4PoflUNL9DaDM8pJeTVRWj0cGvyIYtV_RYp1l_PpFe5ZH2RnertvGeYYIn8Uoi8J5enSZppjouUgJBL2MlFoNIg==&xkcb=SoCE6_M3jGpHfWynHB0YbzkdCdPP&camk=UoKtGZLa3XJ4m_FGbRlp_w==&p=19&fvj=0&vjs=3'}, {'title': 'Advanced Processing Cleaning & Contamination Engineer', 'company': 'ASML', 'location': 'Wilton, CT 06897', 'advantages': 'Full-time', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0A_DDXJpBmDjNhNk0SonxKsswszHAivHatKsF66mYTBcjMCkA3gOusSnWExovNLN1X8PTa0De43C3KFp5IJt6wxmNV2mSeU0NNQM8j-q172-JyIy5J0ekH2Johgq5VCuYOW6u9jBb9snDOErkq-ME0lI6NyJ_izkRGoN8TCo7zb3lNUADInRiSbpXcKW5npUruVnbBtOPrgLiDbLTEYTJvtrP01lfSs19V-Qt_E8wBWDVIyi6BIV8LuFYD2JKjq7JxjbWJeVYG4PEFf56DnK_1Br2vOOVp9q8TjikHS8l7ibhddIJfF8ym7UL4T83xzg2agw7rwD5qdsgkiQEB9OJZA5YAIboLy_oxEAgILFz70krxIuCnEF9xjmpVRUQEidR-jjwmjlGOQWREgoD_CLJP8M1ojw-yO6xDKKhYLle3LQa6vPgXJc_BfVLRYLiHhjwZsWjkrNFX--ebly42VX7MX0DRkERHQqllsJAlGO54vFaT98jX1NXql5OMSfm7qUDqr3KYnPOBDC41KawwuX7g0FXK5XrrJXU6MCG2lIYVRkRMKfhBwnrsdDyIohHmMI6tqzZ3d07Qw6UhbLTL9V-nrn8NIZoCSkfelXIRlwB3HkPm8VCzSNm487NHV0Euwc88N4qHKOzvhxPfZLvNDgV-2mig0xW8hFWiypA_7Ks8XNJButp70BzT3BbwDUDvQyn0aJJding44n7cKHMDOufyO0im30SEwEnK2BiCH9Jc_rhDP6cXuxaSe0fiosBc3amSFT_7L4tnb_vdQNItDaBEGze9PMtlOfDQ=&xkcb=SoAK6_M3jGpHfWynHB0fbzkdCdPP&camk=C3EPSzFlQw-snIlGi5a3BQ==&p=20&fvj=0&vjs=3'}, {'title': 'Data Engineer (Data Management)', 'company': 'MetroPlusHealth', 'location': 'Hybrid work in New York, NY 10261', 'advantages': '$120,691 - $130,691 a year', 'job_link': 'https://www.indeed.com/pagead/clk?mo=r&ad=-6NYlbfkN0AeRVrdO2nL2D9lRfc_oaOvAbfkg761u5f3B67ZAhr2d6q-2bhhGFgBEwKfqdDxFVDpM0iWZKex2mmT0Q0fiLTBLA55iPb-DhcdvdI01Fh8cOEdNgk_HNNuzF29M9X0DNThXcNUyeKLyNcou_prZVDsTwEPsQzLQ_NWa_w8lO5u1ZF18IZNPiarubKxPWUV0FtQnG2LgeH__crSRXP1CeWMFSBBVLbyWKfME2tF9NPZESm5KZzx8_dkN5GTc6yEMlPz0G_VOXXKQ7RJ1z1d3MtwSbNUMW9O9DNECITjV4xwlupH4oGQWyPRcfgn8HxDX0qcofrZqaoDJxt-pnrA2oZSnUiPNaWIeJ_fn31rzj7riJCvt9LTG0XiHcmgtz7kplW2VsyUPuJHxmgQXqhmdx76nu-US3SgfJjkTXS-otwpZ_gpXUgbUN2xQIOtE1fWcw7o2IzLwhbQNZ9Bfw8tHvvafo9jCELRvO6Up7R-qYMG2yxq-Ae5a3OPKf7S0TgXJTDFIrIFUl-6f8_JvBZ502wunu9xBQOddmAnk3Wy5boNOw7ZOGXix_5kqcKLkRWR0Gg0U37dktGb3qb5r0krN-stZBZ6Nlx-Q0R6TyXYw5KmCuMVEmBa2ZYHERRHxz7CdjjpuZ2pUSJoFbdIut5De9DshH1d2gUbntArUqKZiNSMXEpevNqp55JG&xkcb=SoC-6_M3jGpHfWynHB0ebzkdCdPP&camk=G42KULXkIwOWYREtTJp1QA==&p=21&fvj=0&vjs=3'}, {'title': 'Actuarial Analyst', 'company': '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'}]
Handling Pagination
When you click on the next page on Indeed, it redirects you to a sign-in page.
It means only the first page can be scraped.
Scraping Job Overview page from Indeed
For this section, we will use this job page from Indeed.
We will scrape:
- Name of the Job
- Name of the company
- Location of the company
- Job Type
- Salary range
- Skills required for this job
- Minimum education required for the job.
The name of the job is inside h1 tag.
The name of the company is inside div tag with attribute data-company-name and value as true.
Location is wrapped inside a div tag with attribute data-testid and value as inlineHeader-companyLocation.
Job type is wrapped inside div tag with class css-1fajx0z.
Salary range is wrapped inside a div tag with id as salaryInfoAndJobType.
Skills and minimum education required are wrapped inside the same class js-match-insights-provider-u74ql7
import requests
from bs4 import BeautifulSoup
url = "https://api.scrapingdog.com/scrape"
params = {
"api_key": "your-api-key",
"url": "https://www.indeed.com/viewjob?jk=1498699c37f1418e&tk=1jo435dgmjo21800&from=serp&vjs=3",
"dynamic": "false"
}
response = requests.get(url, params=params)
obj={}
if response.status_code == 200:
print("Success! Page fetched.")
soup = BeautifulSoup(response.text, "html.parser")
# Job name → h1 tag
title_tag = soup.find("h1")
obj["title"] = title_tag.get_text(strip=True) if title_tag else None
# Company name → div with data-company-name="true"
company_tag = soup.find("div", attrs={"data-company-name": "true"})
obj["company"] = company_tag.get_text(strip=True) if company_tag else None
# Location → div with data-testid="inlineHeader-companyLocation"
location_tag = soup.find("div", attrs={"data-testid": "inlineHeader-companyLocation"})
obj["location"] = location_tag.get_text(strip=True) if location_tag else None
# Job type → div with class "css-1fajx0z"
job_type_tag = soup.find("div", class_="css-1fajx0z")
obj["job_type"] = job_type_tag.get_text(strip=True) if job_type_tag else None
# Salary → div with id="salaryInfoAndJobType"
salary_tag = soup.find("div", id="salaryInfoAndJobType")
obj["salary"] = salary_tag.get_text(strip=True) if salary_tag else None
# Skills and Education → both share class "js-match-insights-provider-u74ql7"
# Skills group has aria-label="Skills", Education group has aria-label="Education"
skills_group = soup.find("div", attrs={"role": "group", "aria-label": "Skills"})
obj["skills"] = skills_group.get_text(strip=True) if skills_group else None
education_group = soup.find("div", attrs={"role": "group", "aria-label": "Education"})
obj["education"] = education_group.get_text(strip=True) if education_group else None
print(obj)
else:
print(f"Failed with status code: {response.status_code}")
soup.find(“h1”) — grabs the job title from the onlyh1tag on the page.soup.find(“div”, attrs={“data-company-name”: “true”}) — finds the company name using thedata-company-nameattribute.soup.find(“div”, attrs={“data-testid”: “inlineHeader-companyLocation”}) — finds the job location using thedata-testidattribute.soup.find(“div”, class_=”css-1fajx0z”) — finds the job type (e.g. Hybrid work, Full-time) by its CSS class.soup.find(“div”, id=”salaryInfoAndJobType”) — finds the salary range using the element’s uniqueid.soup.find(“div”, attrs={“role”: “group”, “aria-label”: “Skills”}) — finds the skills section by matching bothrole="group"andaria-label="Skills"together, since many divs share the same class on this page.soup.find(“div”, attrs={“role”: “group”, “aria-label”: “Education”}) — same approach for education, just switching thearia-labelto"Education"..get_text(strip=True) — extracts clean text from each tag, stripping extra whitespace.
Once you run this code, you will get this JSON in response.
{'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'}
Key Takeaways:
- Indeed job listings can be scraped using Python libraries like
requestsandBeautifulSoup. - 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.
