Key Skills You Need to Become a Data Engineer

Key Skills You Need to Become a Data Engineer

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Rashmi Karan
Manager - Content
Updated on Oct 30, 2023 14:07 IST

Looking to embark on a career as a data engineer? Learn about the critical skills you need to master, including programming languages, data modeling, ETL expertise, and familiarity with cloud platforms. Start your journey to becoming a sought-after data engineering professional.


Data engineering and data science work hand in hand and are responsible for making the best out of the available raw and unstructured data. They are the bridging stones for data scientists as they build pipelines that transform the available raw data into formats that data scientists can use. Data engineers optimize the processes of data recovery and develop dashboards and reports. Depending on the organization, data engineers are also responsible for communicating trends in data. This role requires a significant set of data engineer skills, including a deep understanding of databases and programming languages and certain business skills to work across departments.

Must Reach – What is Data Engineering?

Usually, industry biggies invest in both data science and data engineering teams, but in the smaller setups, data engineers are the ones who take up both tasks. Whatever the size of the organization they are working in, data engineers need to have some mandatory skills to succeed in their careers. This article tries to explore some of those crucial data engineer skills.

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Top Data Engineer Skills

Here we have listed some of the most in-demand data engineer skills sought by employers. Let’s take a look at those essential data engineer skills.

Database Systems – Database systems like SQL and NoSQL are among data engineers’ most required technical skills. They should know how to work on and manipulate database management systems (DBMS) for information storage and retrieval.

Data Warehousing Solutions – Data engineers work on huge volumes of data. Since this data is ported from extensive sources, it is important that data engineers are familiar with data warehousing solutions like MarkLogic, Oracle, Amazon RedShift, etc.

ETL Tools – Extract, Transfer, and Load tools, popularly known as ETL tools, are a category of technologies that collect, read, and move raw data from various sources into a database or business intelligence platform. This process helps to extract the right data to analyze and solve a specific business problem, making it a must-have skill for data engineers.

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Machine Learning Algorithms – ML algorithms help to make predictions based on previous data. Since data engineers collaborate with data science teams, they should understand these algorithms to get models into production and build more accurate data pipelines.

Programming languages– Knowledge of Programming languages like Python, Java, and Scala is crucial to the job roles of data engineers.

  • Python – The most popular and useful language for statistical analysis and modeling, as well as ETL tasks
  • Java – Extensively used in data architecture frameworks
  • Scala – A Java extension that is interoperable with Java

Also Read – Top Data Engineer Interview Questions and Answers

Amazon Web Services (AWS) – Data engineers use AWS to design automated data flows to ensure agility, innovation, and scalability.

Apache Hadoop – Apache Hadoop is an open-source framework. It is a collection of tools that support data integration. Hadoop allows storing and analyzing a huge chunk of information and is crucial for running data engineering teams.

Kafka – Kafka is an open-source processing software platform that handles real-time data feeds. Data engineers often use Kafka with Hadoop for real-time data processing, monitoring, and reporting.

Data structures and algorithms – Data structures and algorithms allow organizing and storing data for easy access and manipulation, making it an essential skill for data engineers.

Big Data – Big data refers to huge volumes of structured and unstructured data. Big data is a crucial tool for data science and AI teams. Thus, it becomes essential for data engineers to know how to work with big data and learn to store, process, clean, and extract information from the data.

Automation and Scripting – Data engineers should be able to write scripts to automate repetitive tasks since they have to deal with such huge amounts of data.

Related – Difference between Data Science and Data Engineering : Responsibilities, Tools, and Skill

Business Skills for Data Engineers

In addition to these technical skills, a data engineer must also have certain business skills, such as –

1. Analytical Problem Solving – Addressing high-level challenges with a clear eye on what is important, using the right approach/methods to make the most of time and human resources

2. Effective Communication – Detailing your techniques and discoveries to be technical and non-technical audiences in a language they can understand

3. Intellectual Curiosity – Exploring new territories and finding creative and unusual ways to solve problems

4. Industry Knowledge – Understand how your chosen industry works and how data is collected, analyzed, and used

Data Engineer Educational Requirements

There are no fixed criteria for becoming a data engineer. However, you should fulfill certain educational criteria, which are –

  • Degree in Computer Engineering, Mathematics, or other engineering disciplines with significant experience in programming
  • Master’s degree or postgraduate degree in topics related to Data Science, Big Data, and/or DevOps.
  • Programming experience in Python and Spark, as well as Unix / Linux systems development


Data engineering is growing. Market experts expect many future advancements, paying the way for the high demands of skilled data engineers. I hope this article helped you to know the key data engineer skills that can help you start a career as a data engineer.

Do you think of any more data engineering skills? Share with us in the comments.


Why is programming proficiency important for a data engineer?

Programming proficiency is crucial for data engineers as it enables them to manipulate, transform, and analyze large datasets efficiently. It also helps in building data pipelines and implementing automation processes.

What is ETL, and why is it important for data engineers?

ETL stands for Extract, Transform, Load. It refers to extracting data from various sources, transforming it into a suitable format, and loading it into a destination system. ETL is important for data engineers as it ensures data quality, consistency, and accessibility for analytics purposes.

What are big data technologies, and why should a data engineer be familiar?

Big data technologies, such as Hadoop, Spark, and NoSQL databases, enable the processing and analysis of large-scale datasets. Familiarity with these technologies allows data engineers to work with big data frameworks and leverage distributed computing for efficient data processing.

Why is knowledge of cloud platforms important for a data engineer?

Cloud platforms, like AWS, Azure, or GCP, provide scalable and cost-effective solutions for storing, processing and analyzing data. Being familiar with cloud platforms allows data engineers to leverage cloud-based services and infrastructure for their data engineering workflows.

How can someone develop the skills needed to become a data engineer?

Developing skills as a data engineer involves a combination of education, practical experience, and continuous learning. Pursuing relevant courses or certifications, working on real-world data projects, and staying updated with emerging technologies and industry trends are effective ways to develop these skills.

About the Author
Rashmi Karan
Manager - Content

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio