What Does a Data Engineer Do?

You can think of data engineers as the architects of the data world. They build and maintain systems and architecture to collect, store, and manage data.

For example, they work with ETL (extract, transform, load) processes to combine data from multiple sources into a central repository. Similarly, they build data pipelines, work with databases, and manage data warehouses.

They basically prepare the raw data for analysis so that data analysts and data scientists can do their jobs. This way, they work together to help the company find valuable insights and make decisions that lead to business growth. 

Data Engineering Skills You Must Learn

To perform all those tasks, data engineers need to gain expertise in various processes, tools, and technologies. They also need soft skills to work together as a team and communicate effectively. Here are all the skills you would need to become a pro data engineer:

1. Programming

Aspiring data engineers often ask me, “Is coding important?” And I always tell them that coding is one of the most important skills for data engineers. It is what will help you optimize and automate data workflows as well as improve data quality and reliability. Here are some programming languages often used in data engineering:

Python: It’s good for handling big data, automating tasks, and working with different data formats. It has a range of libraries that make data processing easier and faster.

SQL: It helps manage and query large databases. It’s also needed for data handling tasks like extracting, organizing, and updating data stored in databases.

Java: This language helps you build scalable, high-performance data pipelines. It helps you develop large, reliable systems that handle big data processing efficiently.

Scala: It helps you work with distributed data systems like Apache Spark. It’s specifically designed to handle large datasets while maintaining performance.

R: It helps in data analysis and statistical tasks. You will need it to perform complex data manipulation and generate insights from data sets.

You can go through this data engineering syllabus to learn more about technical skills that are valued in the present market.

2. Data Warehousing

One of the most important roles of data engineers is to store and organize raw data in data warehouses. Data warehouses are simply central repositories that allow access to real-time data for analysis and decision-making. 

Without this skill, you won’t be able to manage the high volume and complex data most companies handle today. So, you need to know about data warehousing solutions like Panopoly or Amazon Redshift. This way, you can make data storage, retrieval, and processing more efficient.

3. Operating Systems

Knowing programming languages isn’t enough. You also need an understanding of operating systems to design, develop, and troubleshoot systems. As a data engineer, you will work with operating systems like Linux, UNIX, macOS, and Windows because data infrastructure often runs on these platforms. For example, Linux is widely used in data engineering because of its stability, flexibility, and performance. 

4. Database Management

This data engineering skill helps you design, maintain, and optimize databases. SQL is the most widely used language for managing relational databases, allowing you to query, update, and manipulate data efficiently. You also need to learn NoSQL databases like Cassandra or Bigtable, which are better suited for handling unstructured data.

You can learn basic SQL queries, cleaning and modifying data, aggregating and analyzing data, working with multiple data tables, troubleshooting and error handling, advanced filters in SQL, data definition language, data manipulation language, using subqueries, creating user-defined functions, etc. 

5. Big Data Engineering

This is an important data engineering skill because you will often have to work with big datasets that traditional databases can’t handle. It will make you an expert at managing and processing data on a large scale.

For this, you can learn Hadoop, which includes topics like MapReduce, YARN, HDFS, data spilling, data replication, Daemons, etc. You have to learn Apache Hive to query large datasets using HiveQL. You also need to know Apache Spark, how to optimize it, and how to process data in real time. A good understanding of real-time data processing with Kafka and its integration with Spark is also important.

6. Azure Cloud Engineering

Microsoft Azure is a cloud platform that provides scalable, secure, and cost-effective data storage and processing solutions. So, this skill helps you build and maintain data pipelines, store data, and run large-scale analytics in the cloud. 

Here, you will learn about Azure services like virtual machines, storage, and database services. Next, you can understand advanced data engineering with Azure and real-time data streaming and processing. Learning hybrid cloud scenarios, governance, and compliance is also necessary.

7. Critical Thinking

This data engineering skill helps you better analyze and evaluate a situation. You need this to identify problems related to data collection, storage, or analysis and then develop effective solutions. You have to come up with innovative solutions to improve the performance of the systems and the quality of the data. This is where critical thinking helps you. 

8. Communication

As a data engineer, you will collaborate with other team members and business leaders with and without any technical expertise. So, better communication skills help you explain data processes and systems and share updates without any misunderstandings. For example, you may have to work with data scientists or analysts and share findings and suggestions. And you know, this skill not only helps you in data engineering but also in your entire life. 

How to Become a Data Engineer

Now, let’s understand how to become a data engineer: