In 2020, an estimated 64.2 zettabytes of data were generated globally, and by 2025, that number is expected to rise to 180 zettabytes. Considering these figures, it’s no surprise that data professionals are in high demand. Those who know how to create the Data Architecture to manage this data are the most in demand – and they are data engineers.

Data engineering refers to the discipline of designing, storing, and analyzing data at scale. Anyone directly involved in building the infrastructure to store, research, and manage data can be called a data engineer.

Given that businesses are generating vast amounts of data every day, the need for skilled data engineers will only increase with time. According to the U.S. Bureau of Labor Statistics, employment for computer and information research scientists is expected to go up by 22% between 2020 and 2030.

A data engineer’s job is very complex, given that they must understand the fundamentals of data collection, processing, and storage. This knowledge helps them build infrastructures that can support data analytics and business intelligence. Their ultimate goal is to make raw data easily accessible by the many stakeholders in an organization.

This article will focus on the granular roles and responsibilities that data engineers are expected to hold and the skills they need to know.

What Do Data Engineers Do?

There are several responsibilities data engineers have in terms of building the architecture for data storage. Some of them include:

• Building Data Architecture: They create a resilient Data Architecture and ensure it is aligned with the business’s objectives. They develop databases for Data Management, work with other teams to understand their requirements and coordinate with backend developers to build it.
• Maintain Software Systems: They design and produce scalable ETL packages used in popular databases. In addition, they maintain several software systems to ensure that there is minimal downtime, as most organizations rely on real-time and sensitive data.
• Data Extraction: Before the infrastructure is created, data engineers need to ensure that they are collecting data from the right sources. They identify where the data is being collected, how it is being collected, and how the entire data ingestion process works.
• Conduct Extensive Research: A vital part of their role is conducting extensive research regularly to understand the different trends in the market, updates to regulatory requirements, availability of new tools in the market, and more. The main objective is to ensure that the company’s data infrastructure is up-to-date as per industry standards and in a way that helps maintain a competitive edge in the market.
• Automate Tasks: They need to have in-depth knowledge of several programming skills such as Python, SQL, R, Java, Scala, and more. This is critical for business operations as companies can regularly generate and acquire large amounts of data, which means they need to automate redundant tasks for categorization, storage, and management. To achieve this, competency in scripting and automation is essential.

Which Skills Do You Need to Know?

The most common route to becoming a data engineer is getting a bachelor’s degree in computer science (or a related discipline). As long as the degree, course, or certificate program teaches you the skills listed below, you can apply for respective roles in the industry—the more advanced the degree, program, or role, the better.

That being said, data engineering requires the knowledge of specific skills, which can be found below:

• Coding: Coding skills are an absolute must in the world of technology, and the data engineering field is no different. It should be your first step, as by learning these languages, you will understand the fundamentals of building Data Architectures and adding functionality to them. Python, Java, R, SQL, NoSQL, and Scala are a few recommended languages.
• ETL: Extract, Transform, and Load (ETL) is how data is extracted and moved into different storage locations. Learning how to do so is critical since engineers work with a host of databases such as SQL, MongoDB, Oracle, Excel, and more. Some of the tools include Xplenty, Talend, Alooma, etc. It’s also important to note that most data will be unstructured, so learning how to work with them is a prerequisite.
• Databases: As mentioned earlier, a considerable part of the job involves working with existing databases and extracting information from them. Most businesses do not focus on creating data because most of what they need exists already. Having in-depth knowledge and expertise on where to look when you have to find something helps you excel in the role. It’s best to become familiar with both relational and non-relational databases.
• Automation & Scripting: Automation is a big task when working with organizations that collect vast amounts of data from several sources. By learning how to script using R, Python, or similar languages, these tasks can be automated, and the focus is on the curated product. A few tasks that can be automated are ETL, report generation, report delivery to stakeholders, etc.
• Data Storage: Data storage is one of the reasons engineers are creating a comprehensive infrastructure. For this, they need to know what kind of data needs to be stored, what type of infrastructure would best serve that data, who will access it, and how it will be accessed. With this information, they can create customized storage options that are in line with the company’s business operations. Examples of storage options are data lakes, data warehouses, etc.
• Data Security: With every storage solution created, knowing how to secure that solution is imperative. Most enterprises have a dedicated security team to handle vulnerabilities and potential data loss. However, they rely on the engineer’s knowledge of the Data Architecture, so they are also tasked with this responsibility. Knowledge of tools such as Apache Hadoop can be handy as it allows encryption of data in secure directories.

Data Engineering for Businesses

Since these professionals are adept at overseeing the entire data life cycle and identifying its value to the organization’s business objectives, they tend to hold much importance. For example, they can extract data from different sources and databases using their niche knowledge, and after processing it, it becomes easier to identify who needs it the most. They can send relevant data to teams such as sales, accounting, marketing, advertising, and more.

In addition, they can analyze existing data to pinpoint potential opportunities to improve business operations and ways to outperform competitors. As most data is unstructured, they make it structured and readable—enabling further downstream processing. By equipping their data with velocity, they can help stakeholders make real-time decisions that are data-driven and accurate. They can recognize the needs of their customer base in real-time and address them.

In terms of business opportunities, they can identify trends in the market based on historical data and predict any behavioral changes. This equips businesses with a sense of preparedness and gives them much-needed time to strategize their path forward. With advanced data analytics skills, they can also recognize areas for self-improvement and implement the recommended changes.

Data Engineering Education

There are several free data engineering courses available from top institutions like IBM, Google, and more. We’ve listed a few of them below:

• IBM Data Engineering Professional Certificate by IBM: The certificate includes 13 courses on topics such as Python, Relational Databases, Linux Commands, Shell Scripting, ETL and Data Pipelines, and more. It requires basic IT skills and can be broken down into 10 full courses, 2 mini-courses, and a Capstone Project.
• Data Engineering Course by Google Cloud: This four-course bundle offered by Google teaches skills such as BigQuery, Dataflow, Data Fusion, Cloud Composer, BigQuery ML, IoT, TensorFlow, Dataproc, and Workload Migration. This certification also helps you build foundational skills for the Professional Data Engineer Examination.
• Introduction to Data Engineering by DataCamp: This four-part course details the differences between a data scientist and data engineer, gives you a base understanding of different tools needed, ETL and its use in data engineering, and a case study on a use case by DataCamp.

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