Google Jobs is a job aggregator that collects data from multiple job listing platforms. If you’re building a job board, scraping Google Jobs should be your priority.
Some other use cases of scraping could be market research, which can further help you track trends in hiring, monitor demand for specific skills, or analyze job availability across industries.
In this tutorial, we’ll scrape Google Jobs using Python and see how to avoid getting blocked when the platform detects scraping.
Let’s get started!
Scraping Google Jobs With Scrapingdog
Whether you are creating a job board, recruitment automation, or analyzing new trends in the jobs market, you need to collect data at a large scale. Additionally, converting raw data into structured format brings out a lot of struggle. For scraping the data consistently, the scraper has to be maintained regularly, without it there is a high chance your data pipeline may get broken after some time.
This is why, a scalable web scraping API is required that can deal with any kind of blockages in the data pipeline. In this section, we will use Scrapingdog’s Google Jobs API with Python to extract job details.
Setting up a Scrapingdog Account
To use the Scrapingdog API, we need the API Key, which will allow us to access the API. Navigate to the Scrapingdog webpage and click on the “Get Free Trial” button to get the API credentials.
After account activation, you will be redirected to the dashboard, where you will get your API Key.
Let's start scraping!!
As we have set the account, we shall now begin with the scraping part.
Suppose you’re a budding entrepreneur looking to understand the job market for data engineers. You want to gather insights about job availability, top employers, and the skills in demand. While looking for options, you found a great option Google Jobs, which aggregates job postings from multiple sources and displays them in search results.
For example, searching “Data Engineer jobs in San Francisco” brings a lot of information including job titles, recruiter, qualifications, and locations — all in one place.
This is where Google Jobs becomes a valuable piece of information. By extracting this data, we can create job boards, analyze job trends, and build a career insight portal for your audience.
Before moving forward, we will install the Python Requests library to create an HTTP connection for API requests.
pip install requests
Then, using the Scrapingdog Google Jobs API, we will target the “Data Engineer Jobs in San Francisco” and extract the details about the listed jobs.
payload = {'api_key': 'APIKEY', 'query':'Data+Engineer+jobs+in+San+Francisco'}
resp = requests.get('https://api.scrapingdog.com/google_jobs', params=payload)
data = resp.json()
print(data["jobs_results"])
If you wish to add more filters and parameters to the API request, then you can read this documentation to start with.
Run this code in your project terminal and you will get all the details about data engineer jobs in San Francisco.
[
{
"title": "Principal Data Engineer",
"company_name": "Atlassian",
"location": "San Francisco, CA",
"via": "LinkedIn",
"share_link": "https://www.google.com/search?ibp=htl;jobs&q=Data+Engineer+jobs+in+San+Francisco&htidocid=9Xb3S06YXXeR9RIgAAAAAA%3D%3D&hl=en-US&shndl=37&shmd=H4sIAAAAAAAA_xXNPQoCMRBAYWz3CBYytWgigo1Wi39gJXiAZRKGbCTOhMwUewnv7Nq8r3zdd9G5Z8scc8UCFzSEK6fMRA228JAAStjiCMJwF0mFlqfRrOrRe9Xikhpaji7KxwtTkMm_Jeg_g47YqBY0GvaH3eQqp_Wqt4KqGRkyw2vm1nC-a5QNnPsfdyoACZAAAAA&shmds=v1_AUFQtON8pYGasi24HNXVKAtRiJFovn9liHF3Q7Q7Xz6oN4_MmA&shem=jblt,jbolt&source=sh/x/job/uv/m5/1#fpstate=tldetail&htivrt=jobs&htidocid=9Xb3S06YXXeR9RIgAAAAAA%3D%3D&htiq=Data+Engineer+jobs+in+San+Francisco",
"thumbnail": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTTIj7S_FsdbFgxCTkMsNTS6PS4BiU-piJi34mp&s=0",
"detected_extensions": {
"health_insurance": true
},
"description": "Overview\n\nWorking at Atlassian\n\nAtlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.\n\nAtlassian is looking for a Principal Data Engineer to join our Data Engineering Team and play a tech lead & architect role to build world-class data solutions and applications that power crucial business decisions throughout the organization. We are looking for an open-minded, structured thinker who is passionate about building systems at scale. You will enable a world-class engineering practice, drive the approach with which we use data, develop backend systems and data models to serve the needs of insights and play an active role in building Atlassian's data-driven culture.\n\nResponsibilities\n• Own the technical evolution of the data engineering capabilities and be responsible for ensuring solutions are being delivered incrementally, meeting outcomes, and promptly escalating risks and issues\n• Establish a deep understanding of how things work in data engineering, use this to direct and coordinate the technical aspects of work across data engineering, and systematically improve productivity across the teams.\n• Maintain a high bar for operational data quality and proactively address performance, scale, complexity and security considerations.\n• Drive complex decisions that can impact the work in data engineering. Set the technical direction and balance customer and business needs with long-term maintainability & scale.\n• Understand and define the problem space, and architect solutions. Coordinate a team of engineers towards implementing them, unblocking them along the way if necessary.\n• Lead a team of data engineers through mentoring and coaching, work closely with the engineering manager, and provide consistent feedback to help them manage and grow the team\n• Work with close counterparts in other departments as part of a multi-functional team, and build this culture in your team.\n\nQualifications\n\nOn your first day, we'll expect you to have:\n• You have 12+ years of experience in a Data Engineer role as an individual contributor.\n• You have at least 2 years of experience as a tech lead for a Data Engineering team.\n• You are an engineer with a track record of driving and delivering large (multi-person or multi-team) and complex efforts.\n• You are a great communicator and maintain many of the essential cross-team and cross-functional relationships necessary for the team's success.\n• Experience with building streaming pipelines with a micro-services architecture for low-latency analytics.\n• Experience working with varied forms of data infrastructure, including relational databases (e.g. SQL), Spark, and column stores (e.g. Redshift)\n• Experience building scalable data pipelines using Spark using Airflow scheduler/executor framework or similar scheduling tools.\n• Experience working in a technical environment with the latest technologies like AWS data services (Redshift, Athena, EMR) or similar Apache projects (Spark, Flink, Hive, or Kafka).\n• Understanding of Data Engineering tools/frameworks and standards to improve the productivity and quality of output for Data Engineers across the team.\n• Industry experience working with large-scale, high-performance data processing systems (batch and streaming) with a \"Streaming First\" mindset to drive Atlassian's business growth and improve the product experience.\n\nWe'd Be Super Excited If You Have\n• Built and designed Kappa or Lambda architecture data platforms and services.\n• Experience implementing Master Data Management (MDM) solutions.\n• Built pipelines using Databricks and is well-versed with their APIs.\n\nCompensation\n\nAt Atlassian, we strive to design equitable, explainable, and competitive compensation programs. To support this goal, the baseline of our range is higher than that of the typical market range, but in turn we expect to hire most candidates near this baseline. Base pay within the range is ultimately determined by a candidate's skills, expertise, or experience. In the United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:\n\nZone A: $203,300 - $271,100\n\nZone B: $183,000 - $244,000\n\nZone C: $168,700 - $225,000\n\nThis role may also be eligible for benefits, bonuses, commissions, and equity.\n\nPlease visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.\n\nOur Perks & Benefits\n\nAtlassian offers a variety of perks and benefits to support you, your family and to help you engage with your local community. Our offerings include health coverage, paid volunteer days, wellness resources, and so much more. Visit go.atlassian.com/perksandbenefits to learn more.\n\nAbout Atlassian\n\nAt Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.\n\nWe believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.\n\nTo provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.\n\nTo learn more about our culture and hiring process, visit go.atlassian.com/crh .",
"job_highlights": [
{
"title":"Qualifications",
"items": [
"We are looking for an open-minded, structured thinker who is passionate about building systems at scale",
...... and so on
That’s how you can easily scrape Google jobs using Scrapingdog’s API without doing any extra work, wasting time, or using additional resources
Conclusion
In a nutshell, Google Jobs is a vast repository of job posts from various trusted sources that can be used to identify industry trends and stay informed about the latest market demands around the world.
In this tutorial, we learned to scrape Google Jobs Results with Python.
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