How to become a data engineer in 2022

The data science and engineering landscapes are constantly and rapidly evolving. In the past, you could rely on a simple database and build scripts with Python and R for data management. However, as data grows, so must we. No matter how much data experience we have as developers, we must ramp up our skills to stay relevant in the industry.

As more startups emerge and companies grow, data inevitably grows exponentially with them. The sources of this data vary and could be in terabytes or petabytes. These sources are bound to multiply before you even wrap your head around them. But worry not; that is where a role as a data engineer comes into play and will help you shine.

This article will introduce you to the roadmap to becoming a data engineer in 2022 and why you should consider it for a career.

We will cover the following:

What is data engineering?
What is the role of a data engineer?
Steps to becoming a data engineer
How long does it take to become a data engineer?
Requirements to become a data engineer
How much does a data engineer make?
Next steps to becoming a data engineer

What is data engineering?

Data engineering is a field where professionals build, design, collect, store, and analyze data. It is a broad field, and many people think of it as a mix of software engineering and data science.

What is the role of a data engineer?

To help you understand better, we will use a plumbing analogy. A data engineer’s job involves moving data from pipeline A to pipeline B, similar to what a plumber does, i.e., moving a water pipeline from the plant to the kitchen sink. Also, a data engineer maintains existing data infrastructure so that it can smoothly convert raw data into usable data. This is similar to how plumbers maintain pipelines to ensure that water flows smoothly when you take a shower or flush a toilet.

In technical terms, a data engineer extracts, stores, and converts raw data into a usable format. They work in sync with other data professionals such as data scientists, business analysts, and machine learning engineers to help make data-driven decisions.

To do all the above, a data engineer needs to be technically proficient in various programming languages and technologies, including machine learning, MySQL, Spark, Python, AWS, distributed systems, data architecture, and so on.

Steps to becoming a data engineer

It is said that a journey of a thousand miles begins with a single step, and this rings true with your path to becoming a data engineer. It will help if you start by researching how you will arrive at your destination. You can begin by reflecting on the following steps.

Step 1- Make a career plan

Because data engineering is an intersection of software engineering and data science, many college students major in computer science for this career path. Several employers also prefer candidates with at least a bachelor's degree in computer science or a relevant field like data science. However, you don't necessarily need a degree to become a data engineer; a data engineering bootcamp, a certification, or a self-study path can equally be sufficient.

How long it will take to become a fully-fledged data engineer depends on which educational path you take and the learning curve that comes with it. We will explore some options with you below.

Step 2: Get an education

Data Engineering education options:

Time estimation: Completing your education can take anywhere from 10 weeks to six years, depending on the program and structure you choose.

Step 3: Get real-world experience

Employers are always looking for data engineers with solid portfolios to back up their experiences. In this vein, thinking outside the box and gaining experience are important. Fortunately, you can do so in the following ways:

Complete an internship
Work on open-source projects and build a personal data engineering portfolio
Actively participate in data engineering hackathons

Colleges and bootcamps usually provide resources for internships and hackathons, but you may want to look into the following resources to be more proactive in your research:

Be sure to update your online portfolio regularly since this will be handy in your job search.

Time estimate: You can spend anywhere from three to six months on this phase, during or after your formal education in data engineering.

Step 4: Earn a certification (optional)

Completing a certification will help boost your credibility in front of some employers. Additionally, some certifications require you to have significant experience in your field; hence, it makes sense to wait to do them later in your career.

A few suitable data engineering certifications include:

Time estimate: You might need a couple of years of experience as a pre-requisite to taking some certifications, but others may not require any. Also, you’ll have to budget for exam fees.

Step 5: Prepare for your job search

Preparing for a job search in data engineering can be intimidating. But if you are well prepared, you will do yourself a favor. Before even applying for data engineering jobs, consider doing the following:

Build a solid portfolio: Volunteer to complete meaningful projects and avoid duplicated work at all costs. You can learn how to create a portfolio online using GitHub and also navigate how to use code repositories.
Prepare for interviews: Prepare for data engineering interviews, become technically proficient in data engineering technologies like SQL, work on problem-solving, and practice soft skills by mastering behavioral interviewing.
Network: Reach out to alums and networks using professional networking and meetup groups like LinkedIn.

Time estimate: This phase may last anywhere from a couple of weeks to several months.

Step 6: Search for data engineering jobs

Congratulations, you are now ready for your job search! You can take advantage of the numerous job boards like LinkedIn, Indeed, Handshake, etc., to take the plunge and apply for relevant jobs.

Don't forget to keep track of your applications via a spreadsheet or a similar application.

Time estimate: This phase is subjective and also the most grueling. It will entirely depend on how often you apply and hear back from companies. Expect anywhere from 3 to 8 months to land a data engineering job. Also, you will experience rejections, but don't let that hold you back.

As Confucius rightly says, “Our greatest glory is not in never falling, but in rising every time we fall.” You will certainly land your first data engineering job as long as you don’t stop interviewing and practicing.

How long does it take to become a data engineer?

Depending on your chosen pathway, it can take anywhere from three months to six years. If you prefer the bachelor's degree route, then you’re looking at four years. Although most data engineers do get their first entry-level jobs right out of college, several others transition from other data-related roles like data analysts to data engineers.

Additionally, the timeline also depends on the following questions:

What technical skills do you have? (Ideally before gaining new data engineering technical skills)
What is your work background? (Anything related to data, like data analytics?)
What are your career aspirations? (e.g., are you targeting senior, mid, or junior roles?)

Suppose you already possess a bachelor's degree in computer science or a closely related field. In that case, transitioning to a data engineering career will be easier, and the timeline will be shorter. However, the opposite is also true; if you don't have either a bachelor's degree or associated technical skills, then expect a longer timeline as detailed above.

Requirements to become a data engineer.

Requirements to become a data engineer vary with existing skill level, educational background, and career aspirations.

Education

As we discussed earlier, you need to learn about data engineering to get a job in the same field. While a bachelor's degree in computer science or a related degree is not a requirement, employers often prefer it. But that does not mean that you can’t become a data engineer in the absence of a degree. We have laid out detailed steps above on different skills routes for you to achieve this goal.

Technical skills

Soft skills

You will need more than technical skills to succeed in a data engineering role. As a professional with multidisciplinary skills, you will be an asset to your company while climbing the career ladder too. Critical soft skills include:

Good communication skills
Strong work ethic
Critical thinking
Adaptability
Collaboration

How much does a data engineer make?

Data engineering is deemed as one of the best-paying jobs in tech and is expected to grow exponentially in the coming years as data explodes and demand for skilled professionals increases.

Dice’s 2020 Tech report listed data engineering as the fastest growing job of 2019, growing by 50% year over year. This trend is only going upward in the coming decades.

According to Glassdoor, the estimated total pay for a data engineer is $118,015 per year in the United States, with an average salary of $97,820 per year. But this is only a median estimate, of course. Your salary could be higher or lower depending on many factors, including location, cost of living, etc.

Nevertheless, data engineering jobs are only growing in popularity and are here to stay. Therefore, there will always be enough room for a pay bump in the long run.

Next steps to becoming a data engineer

Now that you have a comprehensive overview of what to expect if you are considering a career in data engineering, we hope you take the plunge and get skilled with data engineering principles by making a suitable career plan.

With a plan in place, you'll next have to get started on your learning journey, and there is no better way to learn than having hands-on experience. The good news is that Educative has hands-on interactive data engineering courses for learners of all skill levels.

For a data engineering career, it would be great to acquaint yourself with the Python programming language. If you are a complete beginner, the course Become a Database Professional with SQL will help you build a robust foundation necessary to succeed in learning more technically heavy data engineering skills further in your journey.

Happy learning!

Continue learning about data engineering on Educative

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