10 Must Have Skills For Data Engineers In 2025
In this world where technologies are increasing daily, everyone must advance themselves with the demanding skills in high demand among industries. Data engineering is another domain in high demand in the past few years and will increase. It is the process of developing systems to collect and use data, which is used to enable subsequent analysis and data science.

In this article, we will take a look at the most important skills that every data engineer must possess. Be it someone new to data science or an experienced individual looking to change carriers, these skills are necessary for every aspiring data scientist.
Who is a data engineer?
Data engineers are IT professionals who mainly work with multiple settings to develop systems that collect, manage, and convert the raw data into information for data scientists and business analysts to interpret. The main objective of a data engineer is to make the data accessible so that organizations can use it to evaluate and optimize their performance. Data engineers also play a crucial role in building and maintaining databases. They work with various technologies and tools to ensure that data flows smoothly within an organization.
10 must-have skills for data engineers in 2025
There are multiple skills that a data engineer should possess to grow in their career. Lets check out the tools and methods that make up the important skills a data engineer needs:
1. Big Data Technologies
Big data technology is one of the most important skills that every data engineer should have, as a large amount of data is generated every minute and companies have to deal with that data and store it as well. Apache Spark and Apache Hadoop are the two best tools that are used to handle that data through distributed processing. Both of these tools help in saving on the expenses that are spent on storing such big datasets, and they also offer features that help in effectively analyzing them.
2. Data Warehousing
Data warehousing is another top skill that every data engineer should possess. As the interest of businesses in data increases, majority of them have started investing in developing data warehouses that collect and store data from multiple sources regularly. Therefore, a data warehouse is something that mainly allows stakeholders to make well-informed business decisions by supporting the process of drawing meaningful conclusions with the help of data analytics. The data-warehousing building process needs a data engineer to perform the data analysis from various sources.
3. Cloud computing tools
The main purpose of data engineers is to handle the raw data of companies and manage that data. This type of company's data is further hosted on cloud servers. It is important to know about the cloud computing tools needed to work with big data. Some of the most popular cloud platforms are OpenShift, Azure, AWS, OpenStack, and GCP. There are multiple companies that work with public, in-house, and hybrid cloud infrastructures based on the requirements of data storage.
4. Database Management
A database management system is defined as the foundation of any infrastructure that helps data engineers develop, maintain, and design the overall data infrastructure that supports the needs of companies. Therefore, data engineers mainly choose database management systems, and some of the popular choices of data engineers are Oracle, Microsoft, SQL Server, MySQL, and so on.
5. Machine learning
Data engineers need to be proficient in machine learning since it improves big data processing by identifying patterns and trends more effectively. Data engineers can classify incoming data, convert unprocessed information into useful insights, and identify significant patterns by utilizing machine learning algorithms. Data engineers require a solid understanding of statistics and machine learning principles in order to successfully incorporate machine learning into their workflows. Additionally, more complex applications tend to benefit more from more recent frameworks like TensorFlow, PyTorch, and Scikit-learn.
6. Data modeling and schema design
Data modeling is defined as the process of developing a conceptual representation of the data that an organization needs to store and analyze, while schema design involves the development of a detailed blueprint of how the data will be organized and structured within the database. The role of a data engineer is to define the relationships between the multiple data entities. Data modeling and schema design mainly involve defining the data types and constraints that will be used to ensure quality and data integrity. It also supports data analysis and reporting by developing logical groupings of data and defining relationships between the data entities.
7. Real-Time Processing
Data engineers should know of data processing frameworks, as these frameworks are mainly responsible for streaming data. Processing large amounts of data is a complex and even more complex task than processing it in real time. Real-time data processing frameworks are mainly used to process data streams and handle the data as it is generated. These frameworks also allow companies to analyze and respond to data in real time, which is very important for applications like monitoring systems, real-time recommendations, and fraud detection.
8. Data visualization skills
Data visualization is a key skill for data engineers, as it helps turn complex data into clear and understandable visuals for end-users. This skill is essential for presenting insights and findings in a way that is easy to interpret and act on. Commonly used tools for data visualization include Tableau, Plotly, Qlik, Spotfire, Tibco, and others, which allow data engineers to create charts, dashboards, and reports that make data-driven decisions simpler for businesses.
9. Data ingestion tools
Data ingestion tools are essential for managing big data, especially as the amount of data grows. These tools help bring data from various sources into a system where it can be processed and analyzed. As data grows more complex, data engineers need to use these tools and APIs to organize, validate, and transfer data efficiently. Popular data ingestion tools include Apache Kafka, Apache Flume, Apache Storm, Wavefront, and others. These tools ensure the data flow is smooth and reliable for further use.
10. AWS Engineering Skills
AWS skills are vital in the field of data engineering, as many organizations rely on Amazon Web Services to manage and process their data. To excel as an AWS data engineer, it's essential to understand and effectively use key AWS services like Amazon Redshift for data warehousing, Amazon S3 for scalable storage, Amazon DynamoDB for NoSQL databases, AWS Lambda for serverless computing, and AWS Glue for data integration and ETL tasks. Mastery of these tools enables data engineers to build efficient, scalable, and reliable data pipelines tailored to organizational needs.
Conclusion
Data Engineers should possess some essential skills that are required to perform tasks in a more effective way and to solve modern data related problems. These skills include expertise in programming languages like Python. In this article, we covered those very essential skills one should have to advance as a data engineer. It is really important to keep up with the changes in technology, and being good at data engineering is crucial because a lot of companies require this. Learning and using these skills helps data engineers deal with the growing amount of data in today's digital world.