Data engineers are in high demand and represent one of the most promising employment opportunities. In this guide, find out how you can apply for a data engineer job.
Here’s what we’ll cover:
Data engineers are in high demand. The need for qualified candidates in the field grew 50% in 2019 alone, according to the Dice 2020 Tech Job Report. Salaries are growing too, with a 9% year-over-year increase in 2019. There’s never been a better time to enter the data engineering field.
Applying for a data engineering job can be intimidating. Employers look for specific skills from candidates, including SQL, ETL tools, and programming languages like Python. Still, getting the job you want can be fairly easy if you understand the process. In this post, we break down the data engineering job application process in this handy step-by-step guide.
The past few years have seen the rise of “big data.” Modern technology allows organizations to track and store larger amounts of data than ever before. Everything from customer social media activity to citywide traffic patterns to the genetics of cancer can be recorded and monitored. The problem is that there’s simply so much info that new data science tools are needed to learn from it all.
That’s why data engineers have become so popular. Sometimes called data architects, these professionals are responsible for building the infrastructure that supports data analysis. They create and manage databases, develop new tools and techniques for storage and retrieval, and work with data scientists to make the data pipeline more efficient.
In the U.S., there are currently 70% more job openings for data engineers than there are for data scientists. More than 28,000 data engineer roles are open in the U.S. alone, with an average salary of more than $110,000. Globally, there are tens of thousands more jobs available that can be performed remotely for companies like Microsoft, Amazon, Google, and IBM. If you’re interested in entering the field, there are plenty of openings for qualified candidates.
Potential employers will search your data engineer application for proof that you have the skills they want. Here’s how to apply for a data engineering job and prove to the hiring managers that you can do the work they need.
The first step to getting any software engineering role is to learn programming languages. These languages make up the programs and platforms that you’ll use in your new career. The most commonly used languages in data architectures are Python and Java.
If you’re not already familiar with these languages, it’s time to learn. You can teach yourself, or you can take a computer science course that guides you through the process. Many people prefer online courses because they offer structure, feedback, and project ideas.
If your resume looks a little empty, you should take steps to fill it up. If you don’t have a relevant degree or experience in the field, employers won’t be able to tell if you have the skills they’re looking for.
Enrolling in an online bootcamp is a great way to fix that. A data engineering bootcamp will teach you how to build data pipelines, optimize your data collection, and monitor the process. A good course will also assign you projects that you can use as your portfolio for future jobs. That looks great on any resume.
You don’t have to rearrange your life to take a bootcamp, either. Online courses are flexible. You can learn on your own, at your own pace, without sacrificing the quality of your education. You can even get help from industry experts to smooth out your transition into your new career.
No one can help your new career more than an experienced pro who’s already in the field. If you’re applying to data engineer jobs, having a professional mentor can help you succeed.
Experienced professionals understand the ins and outs of data engineering. They can answer your questions and help if you get stuck on a concept. They can even help you find out about jobs that haven’t been posted online. Connecting with a mentor lets you learn from their experience while you’re still applying for your first job.
A critical element of data engineering is the framework you use. Frameworks are predeveloped storage and processing systems that automate elements of the collection process. The majority of data engineering projects rely on a framework’s data lake or data warehouse to extract and transform information.
Common framework systems include Kafka, Apache Spark, and Hadoop. The best way to get experience with these frameworks is to use them—take on a project that uses a framework, and you’ll have a much more intuitive understanding of how it works.
Once you start working on your programming skills, you need to write a strong resume. After all, your resume is the first thing any employer sees. It should explain why you’re an excellent potential hire and convince the hiring manager to interview you.
Include your education, skills, and experience in the field. Having a bachelor’s degree in an area like statistics, math, or computer science is helpful, but it’s not required by many companies. Instead, they prefer to see that you understand data modeling, machine learning, and data analytics. Whether you demonstrate that with education or work experience is less important.
Data engineers need to talk with data analysts, scientists, and stakeholders. That means you need to be able to explain complex topics in simple terms. You’ll also need to collaborate with other engineers on your team. You can work with a mentor and network with other people in your bootcamp to practice your communication skills before you apply for data engineering jobs.
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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