How To Become a Big Data Engineer, Plus Key Skills and Tools

Since the birth of the digital age, the amount of data being gathered and analyzed on a daily basis has skyrocketed. We now live in an age of big data: a term commonly used to refer to the massive amounts of information now available to professionals in countless industries.

That’s where big data engineers come in. If your interests include computer science, algorithms, math, software development, problem-solving, or all of the above, then you may be a big data engineer in the making.

In this guide, we’ll walk you through what a big data engineer does, what’s needed in order to become one, and everything else you need to know to determine if data engineering is the right career path for you. We’ll also lay out the expertise that big data engineers bring to the table for the businesses that hire them.

Table of contents:

• What does a big data engineer do?
• What’s it like to be a big data engineer?
• Learning paths for big data engineers
• Tools, tech, and skills of the trade
• Is a career as a big data engineer right for you?

What does a big data engineer do?

In simple terms, a big data engineer is an information technology (IT) professional who develops technology that turns large amounts of data into actionable insights.

To use a real-world example, imagine that you were a marketing and advertising professional back in the 1950s. In order to promote a client’s product, you might use billboard advertisements, newspaper ads, or create a radio campaign. But how many people actually saw your billboard, heard your radio spots, or read your carefully written ads? Which of your messages managed to convert customers, and which fell flat?

Unfortunately, you’d have no solid way of knowing. Now, cut to today, when business owners can tell exactly how many people clicked on their digital ads, interacted with their social media campaigns, and visited their websites.

While having all that information is useful, a data overload can be just as useless as having no data at all. That’s why companies hire big data engineers to create data mining and optimization tools that transform it all into relevant insights. Let’s take a look at how these data professionals operate by discussing some of their key functions.

Transform raw data

Big data engineers design data pipelines that organize information into useful insights. For instance, they might program a marketing company’s data infrastructure to discover which ads are the most popular among certain demographics—or, to pinpoint the times of day when new social media posts get the most interaction.

Part of the reason that big data engineers are in such high demand is that they use their skill sets to create unique data architectures to suit the needs of each client. For instance, if the same data professional was collaborating with a healthcare company, the odds are much lower that their job would focus on ad tracking. Instead, the health care client might be more interested in building a data-processing framework designed to help with diagnostics or remote patient monitoring.

Collaborate with data teams

Big data engineers often work and collaborate with other professionals, such as data architects, data scientists, and data analysts. Though they sound similar and can have overlapping skill sets, each of these data professionals has their own unique role to play on a data management team.

Here’s a breakdown of each data team member’s role in the data ecosystem:

• Data architects. A data architect works with each client to determine their needs and designs a database architecture framework accordingly. Think of them as traditional architects who design and build the frame of a building.

• Big data engineers. Once the data architect has constructed a solid framework, big data engineers outfit it with data pipelines and refinements. It’s like turning the framework of a house into a fully functioning home.

• Data analysts. Rather than building systems that collect and store data, data analysts deal more with data itself. Often considered an entry-level data scientist, the data analyst is responsible for processing and structuring the data into actionable insights.

• Data scientists. Data scientists also work with data but tend to use more advanced techniques, such as machine learning, advanced statistics, data modeling, and predictive analytics. They often use software tools like MySQL, TensorFlow, or Hadoop to wrangle both structured and unstructured data into useful insights.

Solve data problems

If there’s one thing big data engineers are incredibly skilled at, it’s problem-solving. Here are some examples of challenges that big data engineers solve on a daily basis:

• Developing algorithms that can transform data from a range of sources into digestible insights
• Processing large data sets in real time
• Using data storage tools to organize and maintain huge data sets
• Creating new data analysis tools and methods
• Ensuring the quality and accuracy of the data collected
• Orchestrating various tasks across multiple data pipelines
• Coordinating and managing the execution of various data processing tasks

What’s it like to be a big data engineer?

According to the Bureau of Labor Statistics, big data engineer jobs are expected to increase by 23% from 2022 to 2032, which is much faster than the average 3% for other jobs. More companies than ever before are embracing data-related decision-making, so it’s a safe bet that this is one job that won’t be going out of style any time soon. It also comes with its fair share of perks, such as high average salaries, growth prospects, and the potential for remote work.

Earning potential

On average, big data engineers earn around $60 per hour, but on Upwork, some may charge as little as $30 per hour. Still, most big data engineers on the lower end of the income spectrum pull in six figures a year. Much like other professions, pay depends on many factors, from experience and education to a client’s location.

Industries

One of the highlights of big data engineering is developing a skill set that’s in demand across a wide range of industries. Here are a few examples of how big data engineers use their skills in different industries:

• Finance. From the U.S. Securities and Exchange Commission (SEC) to banks, organizations are using big data to increase financial security and weed out bad actors. Investment banks and asset managers can use machine learning and data analytics to maximize their investment returns.
• Health care. Big data engineering has fundamentally changed the healthcare industry by enabling predictive diagnostics, the digital storage of health records, and digital health monitoring tools.
• Entertainment and media. You know how your favorite streaming app suggests shows and movies based on your personal preferences? That’s big data in action.‍
• Business intelligence. Big data engineering has given businesses more insights into their customers than ever before. It allows for targeted marketing, such as social media ads selected based on your interests and browsing activity.

Work environment

Due to high demand, big data engineer roles tend to be very flexible. While some big data engineers work for large companies like IBM or Microsoft, others enjoy a variety of remote work opportunities like those you’ll find on Upwork.

In some instances, a client may require a data professional to work on-site or on a hybrid basis for security reasons—but you can find plenty of fully remote opportunities if you prefer to work from home.‍

Learning paths for big data engineers

If you’re interested in pursuing a career path as a big data engineer, then a bachelor’s degree in computer science, software engineering, math, or a related field is a great place to start. Many data professionals also go on to earn a master’s degree or specialized certifications, which can go a long way toward enhancing your career prospects.

This is one of those professions where you don’t want to cut corners on education, as it requires a substantial amount of technical knowledge. You’ll need to become proficient in everything from coding and scripting languages to data structures, statistics, and software engineering.

Due to the ever-evolving nature of the industry, certification courses can also be a great way to keep your skills sharp and boost your resume. Here are several great certification options you may want to consider:

• AWS Certified Data Analytics. Amazon’s AWS Data Analytics certification shows clients that you’re a pro with AWS data lakes and analytics solutions.

• Certification of Professional Achievement in Data Sciences. If you’ve mastered basic data engineering skills, then check out this certification program from Columbia University’s Data Science Institute. It will help you take your skills to the next level by deepening your understanding of algorithms, visualizations, machine learning, statistics, and more.

• SAS Certification. SAS is a notoriously tricky yet powerful software program designed to enable powerful statistical and predictive behavior analysis. Earning your SAS certification is a great way to make your resume stand out and expand your toolbox.

Tools, tech, and skills of the trade

One of the reasons that big data engineering tends to be so lucrative is that it requires an impressive skill set. Here’s a look at some of the most in-demand skills you can expect to find in a big data engineer’s toolbox:

• Fluent in programming languages such as Python, C++, and Java
• Mastery of structured query language (SQL) and NoSQL databases
• Solid grasp of ETL (extract, transform, and load) systems and data warehousing
• Data processing and visualization
• Data mining
• Familiarity with Python libraries, machine learning, and AI
• Knowledge of multiple processing systems and cloud storage systems
• Familiarity with tools like the Hadoop ecosystem, Apache Spark, Kafka, Hive, and the MapReduce framework

Technical skills and tools to know

If you’ve yet to familiarize yourself with all of the many technical tools and skills mentioned above, don’t worry. Here’s a quick breakdown of some of the most pivotal concepts that every big data engineer should be familiar with:

• Python. A general-purpose programming language that’s very popular among big data engineers. Not only is it very versatile, but it’s also the basis for a collection of great data analysis libraries like SciPy, Pandas, and NumPy.‍
• Java. An older programming language that’s become a tried and true classic. It’s stable, scalable, and easy to use.‍
• Scala. Another great programming language that’s become very popular among big data engineers. While it requires a bit of a learning curve, it’s designed to handle both functional and object-oriented programming.‍
• Hadoop. Apache Hadoop is an open-source framework that big data engineers often use to distribute and store large amounts of data. With features like distributed storage and parallel processing, Hadoop is a handy tool for breaking large workloads into smaller, more manageable chunks.‍
• Apache Spark. An open-source, multi-language engine and data processing framework. It’s a great tool for data science, ETL, machine learning, and more.‍
• AWS. Amazon Web Services (AWS) is a popular choice for cloud computing, data storage, analytics, and more. It also offers plenty of tools that big data engineers can use to build sophisticated applications.‍
• Azure. Microsoft Azure is another cloud computing platform that offers tools like data lake storage, analytics, and integration. But Microsoft also offers several open-source tools, many of which are based on the Adobe Hadoop ecosystem, in the Azure HDInsight service.‍
• SQL. Structured Query Language (SQL) is a popular programming language that big data engineers often use when working with relational database management systems.‍
• MongoDB. A database management system that works with NoSQL, rather than traditional SQL relational databases.‍
• Cassandra. Apache Cassandra is an open-source distributed database management system used to store large amounts of NoSQL data.‍
• Kafka. Apache Kafka is an open-source stream-processing software platform that's designed to handle real-time data feeds. It's a popular tool among big data engineers for building real-time data pipelines and streaming apps, ensuring efficient data transfer.‍
• Hive. Apache Hive is a data warehousing and SQL-like query language system that works atop Hadoop. It's designed for managing and querying large datasets, especially those stored in distributed storage like Hadoop Distributed File System (HDFS).‍
• MapReduce. MapReduce is a programming model and processing technique for computing large datasets. It breaks down big data tasks into smaller sub-tasks, making data processing more efficient and scalable, especially in distributed computing environments like Hadoop.

Soft skills required

In addition to technical skills, there are several soft skills you’ll want to make sure you hone if you want to succeed as a big data engineer. Here are some of the most important and why they’re so vital to success in a big data engineering role:

• Communication. Keep in mind that not every client you work with will be particularly computer-literate (which might be why they need you). So being able to discuss projects clearly, and in ways they can understand, is important.

• Problem-solving. Problem-solving is an essential quality in data engineering, where you’ll often be expected to adapt to ever-evolving innovations. While strategic thinking is important, this is one area where a fair amount of creativity can also come into play.

• Collaboration. You may often find yourself working with data scientists and data architects with similar skills. In these situations, the ability to delegate roles, take suggestions, and work together is often critical to a project’s success.

Is a career as a big data engineer right for you?

How do you know if big data engineering is for you? Here are several things to consider before setting out on this particular career path.

Timeline

The amount of time it takes to become a big data engineer will largely depend on your current skill level and background. If you already have a strong IT background, then you may be able to make the transition by rounding out your skillset with an online boot camp or certification program.

If you’re just getting started, then you’ll want to pursue a minimum of a bachelor’s degree and may also consider rounding out your skills with a master’s degree or online learning courses.

While there’s no set timeline for becoming a big data engineer, many professionals start out in entry-level jobs like data analysis or database administration and work their way up from there. Working with independent clients along the way is also an excellent way to begin building your portfolio.

Challenges and rewards

Becoming a big data engineer can be a challenge due to the sheer amount of technical skills required. But while this may seem like a con for some people, it can end up being one of the biggest perks of the job for those who enjoy continuously learning and developing new skills. Once you break into the industry, you’ll enjoy plenty of freedom to work with clients in nearly every industry, all while pursuing a lucrative and rewarding career.

Required abilities

As you can see, becoming a big data engineer requires a great deal of education, self-learning, and on-the-job experience. You’ll also want to keep in mind that working with big data is a relatively new profession, which requires adaptability to keep up with a rapidly evolving industry. Determining whether big data engineering is for you will largely come down to both your skillset and your willingness to learn and adapt throughout your career.

Work as a data engineer

In our data-driven world, countless businesses rely on big data engineers to help them translate information into meaningful insights. With the right blend of technical know-how, soft skills, and passion for learning, you too can enjoy a rewarding career in big data engineering. Head to Upwork to begin searching for rewarding big-data engineering jobs that will connect you with top clients from around the world.

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