Are you keen on starting a career in data engineering? In this guide, we will discuss why you should consider getting an AWS data engineer certification.
Here’s what we’ll cover:
Amazon Web Services (AWS) is the leader in the cloud services space, holding a 31% market share. It powers some of the world’s top Internet businesses such as Airbnb, Netflix, Twitch, Reddit, and so on. Many large enterprises such as Disney and Pfizer; and hundreds of thousands of small and medium businesses. If you’re looking to build a career in big data or cloud technologies, AWS is a platform you cannot avoid.
To find a rewarding career in this rapidly growing industry, you need to stand out from the crowd. One way in which big data professionals achieve this is through certifications—an AWS certified data engineer is often preferred by employers ahead of those without.
AWS offers three levels of certification: foundational, associate, and professional. At the foundational level, everyone is certified as a cloud practitioner. At the associate and professional levels, you can choose from solutions architecture, development, or operations.
In addition to this, you can also earn a certification in areas of specialty such as networking, security, machine learning, and data analytics.
Depending on your areas of interest and experience, you can choose from the above options. Given the rapid adoption of big data analytics among businesses globally, AWS big data/data analytics certification is growing to be highly sought after.
Primarily, a certification helps bridge a professional’s skills gap—anyone preparing to be certified as an AWS data engineer is likely to develop skills and experience across the entire landscape. Moreover, certification shows your employer that your skills are demonstrable, helping you grow within your current job, get a better job or negotiate better salaries. Here’s how.
Several cloud and big data jobs today ask for a certification. Take this job at Cognizant in Chicago, for instance. It requires applicants to have at least one AWS certification. Even in positions where a certificate is not a requirement, it is often preferred, like this role at Anderson Loop.
For recruiters, a certification is clear proof of one’s skills in any field. It saves them from having to test the candidate in the basics of the technology, and allows them the time to discuss its real-world applications.
With an active big data specialist certification, you increase the number of jobs you are qualified for. You also increase your chances of being shortlisted for the jobs you desire.
A survey by Global Knowledge found that certified professionals earned an average of $129,000, while those without certification earned less. The same study also found that AWS certifications feature among the top paid jobs in the US.
The Jefferson Frank Salary Survey reveals that 37% of respondents reported a salary rise post-certification—an average increase of 33% in their annual earnings.
As a certified AWS certified data engineer, you can grow to lead teams in your area of work, be it solution architecture, development or operations. On the other hand, you can also gain specialist certifications in analytics, networking, etc., which establishes you as an expert in that niche. With these specialist certifications, you can also propel your freelance career.
Absolutely. AWS certifications do not require applicants to have a formal college degree to apply. It does need you to have relevant experience, though. To take the AWS big data specialty exam, AWS recommends that you have five years of experience in data analytics in addition to holding an active cloud practitioner certification or one of the associate-level certifications.
As per the official site, the AWS big data specialist exam primarily tests you on the following:
While preparing for the exam, focus on these three aspects. In addition to learning the theoretical fundamentals, gain hands-on experience by applying the lessons learned.
You can begin by taking the free online courses that AWS offers on data analytics and big data. With these courses, you will gain an understanding of data engineering on AWS and its technologies such as Amazon S2, Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis, etc. Also, read recommended whitepapers and frequently asked questions to understand real-life applications of AWS big data technologies.
However, these free courses and reading might not offer comprehensive knowledge and hands-on experience with data engineering, that you need to be certified as a data engineer. For instance, the resources on AWS often assume that you have a basic level of skills in data engineering and focus on the tools and tech on its platform. If you’re a beginner, you need a foundational course. An online bootcamp such as the Springboard Data Engineering Career Track offers a mentor-led, project-driven approach to big data analytics, giving you a roadmap for earning an AWS certification.
Once you have a clear understanding of the theoretical foundations, apply them to practice for hands-on experience of data engineering with AWS. Solve real-world business problems using AWS’s tools and technology, learn from them, and optimize as you go along. Zymergen’s Dmitriy Ryaboy curates an excellent list of data engineering projects you can try your hands at today.
AWS offers a practical readiness assessment to help professionals qualify without having to attempt the exam multiple times. This assessment—offered as both classroom training or self-paced digital training—reviews study questions to show you how to interpret the concepts, identify the correct options, etc.
The AWS big data specialist exam is a multiple-choice/multiple answer test conducted over 170 minutes. It is typically conducted at a test center and is available in English, Japanese, Korean, and Simplified Chinese.
Ready to switch careers to data engineering?
Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.
You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.
Check out Springboard's Data Engineering Career Track to see if you qualify.
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