Data engineering is a fascinating and fulfilling career – you are at the helm of every business operation that requires data, and as long as users generate data, businesses will always need data engineers. In other words, job security is guaranteed. 

But, with such great power comes great responsibility. The journey to becoming a successful data engineer features tricky terrain that you need to navigate and get right from the start. In this short and to-the-point article, I’ll walk you through the entire process of becoming a data engineer, helping you dodge the common pitfalls. 

It’s not rocket science, but it’s no walk in the park either.

What Are the Roles and Responsibilities of a Data Engineer?

Before we get into the nitty gritty of data engineering, you must understand what a data engineer is in the first place.

Simply put, a data engineer builds and maintains data pipelines in various environments to make them easily accessible when needed. This includes designing, developing, and constructing, as well as testing data architectures in expansive processing systems.

Here is an overview of some data engineering roles and responsibilities.

Data Collection: Facilitating data collection is probably the primary role of this career, regardless of the data engineer stack. Data engineers collect various sets of data from multiple sources to help their companies get better, real-time insights into a specific market segment. Data collection can also facilitate:

• Insights into consumer behavior
• Insights into performing and underperforming marketing strategies
• Product or service customization
• An upper edge over competitors

The kind of data that the engineer collects depends on the industry, local policies, regulations, and overarching business objectives and goals.

Raw Data Analysis: Raw data includes any data set that isn’t ready for use. This includes data sets collected from multiple sources and aggregated in a centralized location. It can be in cloud software or an on-premises database, depending on the company culture.

Businesses often employ data engineers to analyze this data to understand which information can or cannot be used. Raw data can act as a primary information source for BI systems, especially when analyzed correctly. Raw data in BI systems helps enrich user personas for a more customized marketing and service delivery.

Data Model Creation and Pattern Identification: A typical data collection process starts with creating a data model that will facilitate easier pattern identification. Data engineers are trained on how to create these models in the following steps:

• Data source selection
• Data sets selection
• Attributes, columns, and metrics selection
• Building a relationship tool to glue various data sets for easier analysis
• Building hierarchies for simplified data validation
• Assigning roles and permission for data model access
• Deployment of the data model

The data engineer assigns a name and description to the data model so that other users can easily navigate the storage server.

Conduct Research and Day-to-Day Tasks Automation: Innovative technologies, such as automation, make data collection easier and more efficient in modern business practices. It’s the responsibility of the data engineer to determine which workflows can be automated to complement a seamless data collection routine.

Data engineers can leverage AI and programming to automatically capture data from consumer actions, paper documents, or even employee routines. The software then transforms this data into easily readable files, which are then compiled for later in-depth analysis.

Solving Business Problems: Bad data is the biggest problem facing modern businesses. IBM estimates that the U.S. economy loses up to $3.1 trillion every year due to bad data. What’s more, 88% of businesses cite that data is the root course of nearly all their problems, sometimes eating up to 12% of the annual revenues.

These statistics point out what data engineers deal with every day. They have to work closely with other business executives and routinely monitor the collected data to ensure that it doesn’t adversely affect the business. Also, data engineers brainstorm and propose ideas that can help solve any other business problem.

Ensure Data Governance Compliance: Every business is responsible and accountable for the data they collect from consumers or any other source. Moreover, there are laws and regulations in every jurisdiction, highlighting to what extent a company should be responsible for its data. Data engineers ensure that their employers comply with these regulations. This can be through ensuring utmost data transparency, quality, and consistency, as well as standardization.

How to Become a Data Engineer: A Roadmap

Are you interested in becoming a data engineer? A recent Data Science Inventory report ranks data engineering as the fastest-growing career by demand, even when compared to its close counterpart, data science. The report reveals that the number of interviews for data engineering jobs grew by 40% in 2020, as opposed to data science, which registered only a 10% growth.

It’s no surprise, then, that data engineering is a career that you would want to explore. Here is a data engineering career path that will lead you to this profession.

1. Get a Data Engineering Education

You’ll need to earn an undergraduate degree to become a data engineer, regardless of your preferred industry. Some undergraduate majors that can lead you to a data engineering career path include software engineering, informational technology (IT), and computer science.

It’s worth noting that data engineering closely intersects with math and science. With that in mind, you can also become a data engineer if you get an undergraduate degree certification in any IT-related course that intersects with math and computer science. This requirement is mandatory for most employers seeking to hire data engineers.

2. Learn Must-Have Data Engineering Skills

A data engineer must possess a set of technical skills to complement their day-to-day roles and responsibilities. Although various job positions might require specific technical skills, some capabilities cut across all data engineers, whether experienced or entry-level professionals.

Some of the technical skills required to complete the roadmap for data engineer include:

• General Coding and Programming Basics

The general basics of a data engineer include exemplary mastery of SQL. This skill will help you manage a relational database management system (RDBMS), as well as issue optimized queries. It will also help if you’re conversant with the data engineer stack, as far as coding is concerned.

For instance, you have to understand the Python coding language and some of the libraries that leverage it, including Matplotlib for data analysis and Pandas for data science. Other Python libraries include NumPy, SQLAlchemy, Pyglet, and SciPy.

On top of that, you’ll need technical know-how skills based on the Linux operating system (OS). This system offers greater hardware support and exemplary computing speed on Windows when it comes to data engineering. 

• General Data Engineering (DE) Basics

Another set of skills that you should have to succeed in this career includes general data engineering basics. For instance, it will help if you understand how data warehousing works, including building and maintaining the infrastructure. This will help you aggregate raw data collected from multiple sources.

Moreover, you’ll need to understand how big data tools work when it comes to data modeling. Some of the tools that you’ll likely work with when modeling data structures include Hadoop and MapReduce, Apache Spark, Apache Hive, Kafka, Apache Pig, and ETL systems. Most importantly, don’t forget to hone your skills in database visualization and management, as well as testing.

3. Possess Essential Soft Skills 

With increasing focus on work culture, soft skills are non-negotiable for most companies today. A good employee should possess a set of not only technical skills but soft skills as well. Some of the soft skills needed to become a sought-after data engineer in the market include:

• Teamwork
• Honesty and accountability
• Passion
• Proficient communication skills
• Critical thinking skills

4. Work on Your First Project

After fulfilling the above requirements, you’ll need to work on a data engineering project to gain relevant experience before venturing into the job market. It’s worth noting that most employers will more likely hire data engineers with some experience, compared to someone right out of the classroom.

That said, look for entry-level jobs as an IT assistant. It can be in your college or any other small company within your city. The goal is to work next to an established data engineer so that you can learn proactively on the job. As you get along with the first project, ensure that you hone your programming, coding, and any other technical skills to attract potential employers.

5. Land Your First Job

Getting your first job will be easier if you have all the requirements on top of relevant experience. You can watch out for job postings on various recruitment sites and apply for them. You can also sign up for job newsletters to get notified in case a position requiring a data engineer emerges. Alternatively, you can cold-pitch giant tech companies, especially if you have something out-of-the-box that can get their attention. 

6. Further Your Career in Data Engineering

Just like in any career, you’ll need to advance your data engineering skills if you’re going to be in charge of complex projects or even offer consultation services. Here are some of the skills and certifications that can help you further your data engineering skills.

• Proficiency in Amazon Web Services (AWS)
• Skills in machine language (ML) and distribution systems
• Technical know-how with data visualization tools
• Big data knowledge and background

Wrapping It Up

Data engineering is arguably one of the fastest-growing positions in the technology sector, thanks to the rise of big data and data science applications. 

And with the increasing demand, today, data engineering is a lucrative career. According to Glassdoor, the average data engineer in the U.S. earns over $110,000 per year. And an experienced data engineer working for a giant tech company can earn as much as $150,000 per year.

Leverage this guide to start your career in data engineering and set yourself up for success!