Data Engineering Explained: What It Is & How to Break In

Table of Contents (TOC):

• Introduction
• Key Takeaways
• What Is Data Engineering?
• Top Data Engineer Skills
• How Does Data Engineering Work?
• How To Become a Data Engineer
• Conclusion
• FAQs

Introduction

On average, a data engineer earns around $137,105 per year, and this number could go up to $211,165 for senior data engineers.

This is also one of the fastest-growing fields, currently at USD 105.38 billion in 2026, expected to reach USD 213.07 billion by 2031.

So if you are looking to enter a high-paying field with expanding future scope, it’s time to develop some data engineer skills.

But first, let’s understand ‘what is data engineering.

Key Takeaways:

• Data Engineers are one of the highest-paid data professionals with an average annual salary of around $137,105, according to Indeed.
   
• Data engineers are responsible for creating data pipelines, data storage systems, and managing big data.
   
• The top skills needed include Python, Java, R, Scala, SQL, NoSQL, relational and non-relational databases, ETL systems, automation, scripting, data analytics, data security, cloud computing, and more.
   
• To become a data engineer, one must learn the skills, get certifications, build a portfolio, and have some working experience in the data field.

What Is Data Engineering?

Data Engineering refers to the process of designing and developing data systems to collect and store data. This data can then be used by data scientists and data analysts for various purposes such as decision-making and machine learning.

Without data engineering, organizations would not have reliable data at hand. This would not only make a lot of the decision-making process slower, but it would also affect the way AI and ML models are trained.

Essentially, data engineering helps organizations be more efficient, save time and cost, and better collaborate with teams to grow a business.

Top Data Engineer Skills

To become a data engineer, here are some skills you need:

• Coding: You will need to have an understanding of programming languages like Python, Java, R, Scala, SQL, and NoSQL.
   
• Relational and Non-Relational Databases: Knowledge of these is important as databases are commonly used for data storage.
   
• Data Storage: Other than databases, data is stored in data lakes, data warehouses, data lakehouses, and more. Having an understanding of which data is stored is important.
   
• ETL Systems: Tools like Google Cloud, Integrate.io, Stitch, and Talend will be used in your role, so having an understanding of these ETL tools is essential.
   
• Automation and Scripting: You must be able to write scripts to automate your repetitive tasks, especially when working with big data.
   
• Data Analytics and BI Systems: You must be able to identify trends and patterns in data and understand its purpose for a business.
   
• Big Data Tools: Familiarity with tools like Hadoop, MongoDB, and Kafka is essential when working with big data.
   
• Cloud Computing: Start with AWS, Azure, and Google Cloud as these are the most popular cloud computing services organizations use.
   
• Data Security: Working as a data engineer, you must be able to securely manage and store the company data you are working on. Having basic knowledge of data security will help.

How Does Data Engineering Work?

To work in data engineering, you must be familiar with the work that is done in this scope. Here is a breakdown of the same:

1. Data Pipelines

The primary deliverable of a data engineer is a data pipeline. You must be familiar with one of these two frameworks: ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform).

• Extract: You will be responsible for writing scripts to gather scattered data from user-facing apps, CRM tools, IoT sensors, third-party APIs, and more.
   
• Transform: Next, you must clean and shape this data. This step also includes enforcing data privacy rules such as masking users’ credit card information.
   
• Load: Finally, the cleaned data is stored into a centralized storage system, making it easily accessible for data analysts and AI models.

2. Data Infrastructure

Apart from the data pipeline, your data engineering skills will also be used to build data storage facilities. This will include helping the company build data lakes, data warehouses, and data lakehouses.

3. Managing Big Data

Standard databases are not built for handling big data. One essential data engineering use case is managing big data using either batch processing or stream processing. This allows the data to be processed easily through multiple computers working together rather than the entire load being on one machine.

4. Data Governance

One huge responsibility within the scope of data engineering is data governance. A data engineer’s day-to-day job involves keeping the system alive and trustworthy by ensuring that data quality standards are always met, that all data can be traced back to its source, and managing cloud scalability.

How To Become a Data Engineer

If you want to know how to become a data engineer, here is the path you can follow:

Step 1: Get Data Engineering Skills and Certifications

Start with the fundamentals. Get your basic knowledge of coding languages, databases, and ETL systems. Then get some certifications to add to your credibility.

You can find these free short courses with certification at UniAthena to make your journey easier:

• Diploma in SQL: Beginner to Advanced Levels
   
• Basics of Python
   
• Diploma in Data Analytics
   
• Mastering Power BI - Data Modelling & DAX
   
• Basics in Machine Learning

Step 2: Build a Data Engineering Portfolio

Get your hands on some data engineering projects and gain practical experience. You can find projects on sites like Kaggle, ProjectPro, GitHub, and more. Add these projects to your portfolio so that recruiters can get a real understanding of what you can do.

Step 3: Get Working Experience in the Data Field

Having experience in the data field will be beneficial when looking for a data engineering job. Your experience as a data analyst, database developer, or database administrator will be a stepping stone to your data engineering job.

These roles will also help you get practical experience in using a lot of the skills you will need in your data engineering career.

Step 4: Become a Data Engineer

Finally, showcase your data engineering skills on job search websites like LinkedIn and Indeed and start applying for jobs.

Conclusion

Data Engineering is one of the fastest-growing careers in the future. While most professionals opt for a data engineering degree when they map out their career path, even without a specialised degree, you can break into a data engineering career.

A degree in engineering, data science, machine learning, or any related field will help you get your foot in the door. Get the right skills and practical experience, and you will be ready to become a data engineer.

FAQs

Q1. Will AI replace data engineers?

A: AI will not replace data engineers, as these professionals are essential in building systems that support the training and learning of many of these AI models. Human expertise is key to performing the tasks of a data engineer.

Q2. What exactly does a data engineer do?

A: Data engineers build data storage systems for data to be stored in. They also manage data pipelines to ensure proper management of big data. Apart from this, data engineers are also responsible for ensuring data security and governance.

Q3. Is a data engineer an IT job?

A: Yes, data engineers are IT professionals responsible for technical design, creation, and management of data infrastructure. They enable other IT and data professionals like data analysts, data scientists, AI engineers, and more to do their jobs in an efficient and effective manner.