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The rise of data engineering: common tools and skills

In the age of content overflow, data is becoming the most valuable currency that companies collect and engineer to retain their competitiveness in the market. Enterprises increasingly rely on these massive data sets, more commonly known as big data, to generate meaningful information for strategic decision-making processes.

Although data scientists are the ones primarily associated with data analytics, big data has given rise to a new field known as data engineering. Much like software engineers, data engineers are responsible for building, integrating, and managing data from multiple sources to build database infrastructure. As such, they play a crucial role in maintaining and optimizing the big data ecosystem.

Because of the technical nature of their job, data engineers use specific tools and programming languages and require concrete skills to carry out their tasks. Below we have listed some of the most common tools and skills that are in high demand for data engineers today.

Data engineering tools

When building a data warehouse, data engineers utilize ETL (extract, transform, load) to extract and move data into the system. These three are combined into a single process by use of tools and programming languages.

Naturally, the exact tools required for data engineers vary from role to role and especially between industries. While the attitude is often “the more the merrier”, there are plenty of resources that aspiring or current data engineers can use to acquire new tool knowledge. Some of the programming languages and software tools most commonly used are:


Python is one of the most popular programming languages, and it is just as widely used in the data engineering community because of how easy it is to learn and read.

The Python community has created a range of Python ETL tools that are readily available for data engineers. Some of them can be used to manage each step in the ETL process, and others are specially designed for a specific step.


Structured Query Language (SQL)

SQL is known as the “lingua franca” of data analysis. Although in today’s highly advanced world of data analytics SQL is no longer the most elegant or fastest way of communicating with databases, it is still the industry standard for data creation, manipulation, and querying in relational databases.

Similar to Python, SQL is as popular due to its ease of use and portability. Consequently, Python and SQL are requirements for over half of all data engineering jobs listed globally. Other popular tools and programming languages include:

  • Java
  • Scala
  • Spark
  • C++
  • AWS/Redshift
  • Hadoop
  • Azure

Data engineering skills

The requirements for a data engineering job have been accelerating over the last few years. Since there is a variety of tasks that come at varying complexities, choosing which ones to prioritize can depend on a series of factors.

Coding on multiple displays

The fundamental skills for data engineers include:

  • Data modeling. Modeling requires knowledge of how to structure tables, partitions, when to normalize and denormalize data, and how to retrieve attributes. Data engineers need to have data modeling knowledge to understand the data scientist and the organization’s needs to build more accurate data pipelines.
  • Even though data engineers mostly focus on data filtering and optimization, a basic knowledge of algorithms can be useful in understanding the big picture of the overall function of an organization’s data, as well as checkpoints and targets for the business challenge at hand.
  • Programming languages. As discussed before, Python is the most popular programming language used for analysis and modeling. There are other widely used languages such as Java and Scala.
  • Database systems (SQL and NoSQL). The two main types of databases are SQL databases, or relational databases, and NoSQL, basically all non-relational databases. Data engineers need to have knowledge of how to manipulate database management systems (DBMS), which is software that communicates with databases for data storage and retrieval.
  • Data warehousing solutions. Since data warehouses store massive volumes of data that are pulled from multiple sources (such as sales and accounting applications), organizations use data warehouse software as a central repository for integrating all data across the company. This data is then used for analytics, reporting, and data mining.
  • ETL Tools. As mentioned before, all data engineers need to have knowledge of some type of ETL tools to successfully extract, modify and store data.
  • Cloud platforms. Since much of software infrastructure has migrated to cloud platforms, data engineers are commonly expected to have cloud knowledge or experience. In many cases, employers deem cloud platform skills interchangeable or look for experience in at least one as a basis for others.

Final words

Big data is exponentially growing as an asset in virtually every industry. As the demand for skilled engineers that can manage such data warehouses is rising, the tools and skills required for the job are also evolving.

Some of the most commonly used programming languages and tools by data engineers include Python, SQL, Java, Scala, Spark, C++, AWS/Redshift, Hadoop, and Azure.

At the same time, data engineers are expected to possess technical skills like data modeling, algorithms, programming languages, database systems, data warehousing solutions, ETL tools, and cloud platforms.

Overall, the data engineers with the strongest skill sets are usually those who can continue to evolve with the newest trends in technology.

Photographs by Charles Deluvio / Markus Spiske / Christina Morillo

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