Learning Data Engineer Skills: Career Paths and Courses

Data engineering is a profession with skills positioned between software engineering and programming on one side and advanced analytics skills like those needed by data scientists on the other. 

Data engineering requires solid programming skills, statistics knowledge, analytical skills, and an understanding of big data technologies. This guide can help you understand the skills you need to acquire and how to begin this exciting career path.

What does a data engineer do?

Data engineers are responsible for designing and managing infrastructure that allows easy access to all types of data (structured and unstructured). As a data engineer, you will be responsible for designing, constructing, installing, testing, and maintaining architectures, including databases and systems for large-scale processing. You will also develop, maintain, and test data management systems.

Data engineers use their technical expertise to ensure the systems they build are secure, scalable, and reliable—meaning they can handle vast amounts of data and provide it in real time. Data engineering is a rapidly growing field with many lucrative job opportunities. 

Essential application criteria

The explosive growth in the amount of data, the wide variety of data types, and the computing power required to make sense of it fuel demand for people who can design systems for collecting and analysing all this information. Data engineers are in high demand across various industries, from health care to e-commerce to finance to technology. 

So, what do data engineer job postings indicate are essential application criteria? The requirements for a career in data engineering vary between employers. However, there are some data engineer competencies that you’ll see consistently in data engineer job listings. These include:

• Knowledge of distributed systems like Hadoop and Spark, as well as cloud computing platforms such as Azure and AWS

• Strong programming skills in at least one programming language like Java, Python, or Scala

• Good knowledge of relational databases or NoSQL databases like MongoDB or Cassandra

• Strong understanding of machine learning principles, statistics, algorithms, and maths concepts

As a data engineer, you’ll need to feel comfortable with various data-related programs and languages. Some are mandatory, and others are simply nice to have. Here are some of the most common ones:

• Apache Hadoop and Apache Spark

• Python

• SQL

• C++

• Amazon Web Services/ Redshift (for data warehousing)

• Azure

• HDFS and Amazon S3

Programs a data engineer should be familiar with

You should know some of the most popular data science programs to become a data engineer. Some details about the more important programs are listed below.

Apache Hadoop and Apache Spark

These are open-source, Java-based frameworks that allow for the distributed processing of large data sets across clusters of computers. 

Hadoop is a framework for distributed applications that solves the challenges of dealing with large amounts of data. It helps address computationally difficult problems and can be used for batch processing, iterative algorithms, and interactive queries. 

Spark is a fast, in-memory data processing engine with elegant APIs in Scala, Java, and Python. It uses Hadoop clusters through Spark or YARN's standalone mode, and it can data-process in Hive, HDFS, Cassandra, HBase, and any Hadoop InputFormat.

C++

C++ is a general-purpose programming language that emerged from the B programming language and was developed at Bell Labs. Created by Bjarne Stroustrup to enhance C, it has evolved into a language with object-oriented capabilities and is also used to build sophisticated web applications.

Amazon Web Services/Redshift (for data warehousing)

Used to provide database warehousing solutions, Amazon Web Services/Redshift is a cloud computing platform that works along with Amazon S3 buckets and Amazon EC2 instances to store your data.

Azure

Microsoft has made a big move into the cloud space with its Azure platform. It includes tools for storage, computing, analytics, and more.

HDFS and Amazon S3

HDFS and Amazon S3 are two of the most popular cloud-based data storage solutions today. HDFS is an open-source file system built to store large amounts of data in commodity hardware. Amazon S3 is a scalable object storage system that can store one or more terabytes of data per file in a highly redundant manner.

Technical data engineer skills

You’ll need to acquire many skills to be a technically savvy data engineer. The list below details just some of the critical areas you can expect to study as a data engineer, but these may vary depending on the company you work for or the project you're working on.

Database systems (SQL and NoSQL)

Data engineers need in-depth knowledge of database systems (SQL and NoSQL) and data warehousing solutions. As a data engineer, you'll need to know how to extract data from multiple sources, transform them into useful information, load them into a usable format, and present the results to inform business decisions.

Data warehousing solutions

Most of your job as a data engineer will focus on building the infrastructure that helps your company store and access its data efficiently. Most companies use some kind of data warehousing solution to help them achieve this goal, so it’s essential to have experience working with them before entering the field. 

ETL (extract, transfer, load) tool 

You also need a strong understanding of ETL (extract, transfer, load) tools to integrate data from disparate sources, manage large volumes of structured and unstructured data, and develop algorithms.

Machine learning

Many large companies today already use machine learning techniques in some shape or form. As a data engineer, you'll build models that drive these machine-learning applications.

Data APIs

Interacting with data APIs is an essential skill for any technical data engineer. These days, the majority of tools and platforms have restful APIs—and you'll need to be able to interact with these services to build solutions.

If you're working in Python, you'll likely use the requests library as a straightforward way to interact with APIs. However, knowing how to consume APIs in other languages can be helpful.

Python, Java, and Scala programming languages

Technical data engineers often work on polyglot teams, especially in the big data space. Python, Java, and Scala are these teams' most common programming languages. To become a technical data engineer, you'll need expertise in at least one (or ideally all) of these languages.

Understanding the basics of distributed systems

Technical data engineers write code that runs on clusters of hundreds or thousands of machines, and, therefore, you need to understand basic concepts related to distributed systems. This includes knowing about coordination protocols, consensus algorithms, and message brokers. 

Knowledge of algorithms and data structures

You need to deeply understand how the different algorithms work to select them appropriately, and the same applies to data structures. You need to choose a suitable data structure that fits your needs. Bad choices can lead to significant performance problems or unexpected behaviour in your systems.

Essential workplace skills for data engineers

Data engineers are critical members of any big data team. While all the listed technical skills are essential, non-technical skills such as communication, collaboration, and presentation are valued more than ever. These workplace skills help you work more effectively with others in technical and non-technical roles, which helps your company achieve its business goals.

Communication skills

Data engineers must be able to communicate with both technical and non-technical colleagues to understand their goals and needs. You must also explain complex processes simply so stakeholders can understand you. This is especially important for explaining results or insights uncovered in your data engineering projects. Without clear communication processes, tools and discoveries can remain underutilised.

Collaboration

Collaboration is another critical workplace skill for data engineers. You must work well with teams of other data engineers, data scientists, or other subject matter experts (SMEs) to build out the infrastructure necessary to support a company's business goals. Knowing how to collaborate and facilitate group communication is vital to your success in this role.

Presentation skills

Data engineers often need to present the results of their projects. This means they need to be able to explain technical concepts in layperson’s terms and make convincing arguments for why a team should take specific actions based on the results of their work.

Different data engineer job titles 

In a constantly moving world, many things are uncertain. However, one certain thing is that companies wanting to be competitive need to collect and organise data and make sense of it. Data engineers have different names and different levels of role. Here is an overview of some of the job titles a data engineer might have and their average salaries as of February 2024: 

• Data engineer: ₹8,87,255 [1] 

• Senior data engineer: ₹17,82,091 [2] 

• Data warehouse developer: ₹4,68,690 [3]    

• Enterprise architect: ₹34,18,165 [4] 

• Data architect: ₹21,51,253 [5]

• Team leader IT: ₹13,36,966 [6]

How to become a data engineer

Your path to a job in data engineering varies depending on your background and experience. You will need a relevant degree, certificates and certifications, and demonstrable experience. 

Consider a bachelor's degree.

The most common, yet not always mandatory, educational requirement for becoming a data engineer is to acquire a bachelor's degree. While there are many options, most employers want to see that their potential candidate holds a bachelor's degree in computer science, software engineering, maths, or related fields.

Acquire professional certificates and certifications.

To be successful as a data engineer, you need to be proficient in programming languages such as Java, Python, or Scala. Consider acquiring certifications or certificates to ensure your industry knowledge is up-to-date and relevant. Some of the certificates that can give you an edge over the competition include: 

• IBM Data Engineering Professional Certificate

• IBM Data Warehouse Engineer Professional Certificate

• Cloud Data Engineer Professional Certificate

If you are considering data engineer certifications, the following should be on your shortlist.

• IBM Certified Solution Architect: Cloud Pak for Data v4.x Certification

• Amazon Web Services (AWS) Certified Data Analytics – Specialty Certification

• SAS Certified Big Data Professional Certification

• Cloudera Data Platform Generalist Certification

Build relevant experience.

Working on projects is one of the best ways to boost your experience as a data engineer. Your work experience largely determines your value as a data engineer. In the interview, employers will likely look at what projects you have worked on and ask questions about them to determine if you have the skills they need.

Explore and pursue opportunities to build your portfolio. You are more likely to have the competencies you need to win a job as a data engineer if you have diverse project experience.

How do you get experience if you can't get a job?

In a word, practice. One of the most effective ways to gain experience is to practice something. You can do this by doing side projects involving data processing and analysis.

It can be a small scale, but you must have something you can show off to potential employers. Some examples include:

• A personal website with a blog to demonstrate your ability to write documentation

• A GitHub project where you contribute code to demonstrate your coding skill

• An open-source data science project to demonstrate your capability to work with others

• A web application that processes raw data into something useful, such as Kaggle

You should also work on open-source projects that solve "real world" data engineering problems. Here are a few examples:

• Build ETL pipelines with Apache Airflow.

• Store data in a scalable database like Amazon S3 or Google BigQuery.

• Use Python Pandas to analyse data and create visualisations.

• Use Python Pandas to prepare data for machine learning model training.

• Use Spark MLlib to train machine learning models.

• Automate moving data between systems using an API like RESTful API or GraphQL API.

Stepping stones to data engineer roles

If you are in another job but enjoy data, you could transition to a data engineer role. Some of the jobs that most frequently lead to data engineering are:

• Software engineers with a passion for SQL and data

• Data analysts with a passion for programming

• Web developers with a passion for databases and data-driven projects

College grads with some computer science coursework, knowledge, and experience may be able to apply for entry-level data engineering roles.

Ready to start your journey to a professional data engineer career?

The world is awash in data—growing faster every day. Our society has become increasingly dependent on data to make crucial decisions, and the demand for data engineers continues to grow. 

Do you want to learn the skills that will allow you to work with massive data sets and build data-driven applications? You can develop your knowledge and skills online by learning to use Apache Spark, NoSQL databases, Hadoop, and other big data technologies.

Leading professionals in the field design the courses and programs on Coursera to give you exposure and the opportunity for hands-on experience in the industry. An excellent place to start could be the IBM Data Engineering Professional Certificate offered by IBM.