How to Become
a Data Engineer

Education Pathways, Skills, and Certifications

With the rapid rise of big data, companies need more experts specializing in computer science, software engineering, and data science. This guide can provide a helping hand if you’re interested in becoming a data engineer.

Read below to learn about the top skills for this profession, what this career entails, and the average salaries. If you want to work with real-world data analytics and learn Python for programming among other technical skills, this career is perfect for you. Learn how to become a data engineer with this guide.

What Is a Data Engineer?

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Data engineers find trends in big data and create algorithms to make raw data useful to companies. To do this, professionals set up and maintain data infrastructures. As an expert in this field, you must have core skills in programming languages, data warehousing, data models, and computer science.

What Does a Data Engineer Do?

The key responsibility of a data engineer is to create data pipelines that transport data gathered from multiple sources into one destination. The data pipelines convert raw data into usable information. Data engineers and data scientists use these insights to help companies reach their objectives.

The list below describes a few primary responsibilities of professionals in this career.

Maintains and Tests Data Pipelines

In the development phase, data engineers need to test data pipelines. The aim of this is to determine their reliability and performance. However, these experts also need to maintain data pipelines to ensure they’re always producing optimal results.

Develops Instruments for Data

A professional data engineer will also have strong developing skills. Using programming skills, these specialists create, customize, and manage databases, integration tools, warehouses, and analytical systems.

Builds Data Models and Identifies Patterns

To gather future insights, professionals use predictive data models and apply advanced forecasting strategies. They also use descriptive data models to gain past insights. While fulfilling these responsibilities, professionals direct a large chunk of their time to identifying data patterns.

Data Engineer Education Pathways

If you want to become a data engineer, you can gain expertise in various ways. These methods include joining a coding bootcamp, completing a course, or earning a degree. Take a look at the list below to find out which education pathway works for you.

Data Engineer Degrees

You can earn an Associate, Bachelor’s, or Master’s Degree in Data Engineering. If you don’t have academic or professional experience, an associate program will teach you the basics.

If you have some academic or professional experience, you can aim for your bachelor’s degree. Your bachelor’s degree is pivotal because most employees require at least four years of education. You’ll learn all the essentials of how to become a data scientist.

You can study for your master’s degree once you have your bachelor’s degree. Your master’s degree will help you secure jobs of better quality that have higher salaries.

Data Engineer Bootcamps

Bootcamps are short-term courses that focus on equipping students with practical, job-ready skills. Bootcamps can help you become a data engineer much faster than a degree, but they aren’t always as in-depth. It’s a great option if you already have technical skills but need improvement in specific areas.

You can find the best coding bootcamps in Sacramento and many other states in the US. A few schools that offer popular coding boot camps for data engineering are The Tech Academy, BrainStation, and Coding Dojo. The best aspect of boot camps is that they don’t require as much time and resources as degree programs.

Data Engineer Courses

To gain the skills for data engineering, you can take a course from a massive open online course provider (MOOC). A course can award you with a certificate, and you’ll learn the vocation fundamentals of data engineering.

Completing a course will give you an entry into studying for a bachelor’s or associate degree. One of the MOOCs offering a data engineering course is FutureLearn. This course offers a range of knowledge, from the basics of engineering to effective communication.

How to Become a Data Engineer A Step-by-Step Guide

How to Become a Data Engineer: A Step-by-Step Guide

A data engineer has to constantly keep up to date with industry changes. However, it can be a highly rewarding career if you love management systems, programming languages, and machine learning.

Below is a detailed step-by-step guide for the best way to become a data engineer.


Earn a bachelor’s degree

A Bachelor’s Degree in Data Science will make finding a job easier. Most employers prefer professionals with a bachelor’s degree, so this step is highly recommended. Your bachelor’s program will also equip you with the technical and theoretical skills to become an expert in this field.


Gain career experience

Related professional experience only makes your resume look better. Find entry-level positions that can provide practical experience. By working hard, you can fast-track your career.


Earn a master’s degree

Although this isn’t compulsory, it’s a fantastic reason for companies to offer you profitable, senior roles. Requirements for master’s programs vary. Typically, students need a related bachelor’s degree and a GPA of 3.0 or higher.


Complete short-term courses

You can do this at a bootcamp or through an online course provider. Taking an advanced short-term course is an optional method to improve your hands-on expertise. A short-term course is an opportunity to advance or master specific skills.


Gain professional certification

Professional certification is a concrete way to prove your skills and technical competence. Industry experts design these certifications to give you a competitive edge over other professionals.

Top Data Engineer Skills

To become a successful data engineer, you must have a specific set of technical skills. These skills include an advanced understanding of programming languages, Amazon Web Services, and Apache Spark.

Let’s examine what each of these skills entails.

Programming Languages

A data engineer must understand programming languages like Python and Scala. This knowledge is vital so experts can communicate with the machines they operate. As a data engineer’s job involves computer-based programming, languages help professionals execute technical functions without error.

Python is relatively easy to learn, and most employers require this skill. Scala is an extension of JavaScript and isn’t as simple as Python. However, if you’re working with a Spark framework, understanding Scala will make the job notably easier.

Amazon Web Services and Redshift

Redshift is a data warehousing service that forms part of Amazon Web Services, a large cloud-computing network. You’ll need to understand the functions of Redshift to store and analyze big data. You can also use Redshift to execute large-scale database migrations.

Many employers require competence in Redshift as it’s a fundamental component of data engineering. Understanding Redshift also makes it simpler to use different data warehousing systems.

Apache Spark

Apache Spark is the best data processing framework on the market today. It’s also a favorite amongst data analysts, data scientists, and of course, data engineers. Experts use this software to process tasks on very large datasets. You can also use Apache Spark to send processing tasks to various computers.

Data Engineer Salary and Job Outlook

According to ZipRecruiter, the average data engineer salary is $121,938 per year. This figure equates to about $59 per hour. Data engineering jobs range from full-time, onsite positions to part-time and remote positions. According to the Bureau of Labor Statistics, data science job opportunities will grow by 15 percent by 2029.

Entry-Level Data Engineer Job Requirements

To successfully land an entry-level data engineer job, you need to have a bachelor’s degree. Your bachelor’s degree should be in computer science, statistics, applied mathematics, software engineering, or data science.

Some employers require you to have at least two years of professional experience. However, employers can waive this if you have a bachelor’s degree. Entry-level data engineers earn an average of $66,410 per year.

How to Prepare for Your Data Engineer Job Interview

You need to make a note of all your strengths and memorize them for the interview. Include the tools and systems you’ve used, your soft skills, and your technical specialties. Be sure to research your employer’s company beforehand to understand what they’re about.

These are best practice interview questions for a data engineer job interview.


Data Engineer Job Interview Practice Questions

  • What describes a “data-first” mindset?
  • What is your method for building reliable data pipelines?
  • How would you define the difference between a data engineer and a data architect?
  • Which frameworks and applications do you believe are critical?
  • What popular data engineering maxim do you side with?
Data Engineer Certifications

Data Engineer Certifications

Earlier, we discussed the importance of personal certification as the last step to become a data engineer. Gaining a professional certification gives employers more reason to hire you as you have proof of your expertise. These are a few of the best data engineer certifications.

Certified Professional Data Engineer Certification

If you’re getting a career in tech as a data engineer, this is a highly beneficial certification. This personal certification from Cloudera aims at proving expert skills in data science, data warehousing, building data pipelines, and related technical abilities.

The exam includes several hands-on tasks, and you have to score higher than 70 percent to pass. You can complete this exam online, and the certification makes you more credible to employers.

Google Professional Data Engineer Certification

If you have an in-depth knowledge of Google Cloud Platform (GCP), earning this certification should be a breeze. This professional certification proves your skills and expertise working with GCP. For jobs where GCP is a required skill, you’ll have a better chance of being hired. The exam takes two hours to complete.

IBM Certified Data Engineer–Big Data Certification

Earning this professional certification proves you can solve big data problems and build large-scale data processing systems. As this certification doesn’t test your knowledge of a specific system, it’s valuable when applying for any data engineering role. You can attend a nine-day course to prepare for this multiple-choice exam.

How Long Does It Take to Become a Data Engineer?

It can take between ten weeks and four years to become a data engineer, depending on the path you take. A bachelor’s degree will take four years to complete, whereas a short course or bootcamp can take six months. The more professional and academic experience you have in the tech industry, the less time it will take.

After starting your career, you can develop your skills further. You can do this through studying in a master’s or doctorate program, completing short courses, or gaining personal certifications. Doing this will help you advance your career into senior roles with higher salaries.

Why You Should Become a Data Engineer in 2021

It’s a fantastic idea to become a data engineer in 2021. The job growth has increased over the past years and likely won’t stop. The big data market is also forecasted to grow exponentially. Therefore, there’ll be more than enough vacancies for professionals in this field.

There are many education pathways and personal certifications for data engineers. You can turn your passion for statistics, computer science, and information systems into a profitable career by becoming a data engineer.

Data Engineer FAQ

A professional data engineer should have skills in Python, Scala, Apache Spark, Amazon Web Services (Redshift), and more related analytical and technical skills. If you lack any of these skills, join a boot camp or course that specializes in the areas you need to improve.

Both professions share the same fundamentals. The difference is that data engineers focus on building infrastructures for data generation, and data scientists focus on mathematical and statistical analysis on that generated data.

Yes, data engineering is in demand. According to the Bureau of Labor Statistics, computer and information research jobs, also known as data science, will grow by 15 percent by 2029. This is over three times the average growth rate.

Data engineers create infrastructure for data generation. These professionals build data pipelines to transfer data from various sources to a data warehouse.

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