Data Science

Career Exploration: What is Data Engineering?

Written by John Terra
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Updated on February 24, 2025

Our data-intensive world needs the means to collect and analyze all that information to make it useful. We generate so much data daily that it’s challenging to sift through these volumes just to find the correct actionable information. It’s like trying to drink the ocean through a straw. That’s why data engineering is vital to today’s information age.

This article focuses on data engineering, including its definition, importance, job roles, required skills, and how to become one through a data science course. We’ll even look at what the future holds for it.

Let’s begin by defining what data engineering is.

What is Data Engineering?

Data engineering is the process of collecting and validating quality data for analysis. It’s a vast field that includes data acquisition, infrastructure, mining, crunching, modeling, and management.

Data engineers design and build systems that allow people to collect and analyze raw data from various sources and formats. These systems help people find practical data applications that organizations can use to thrive.

So, what’s the difference between data engineering and data science?

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Data Engineering vs. Data Science

Data engineering and data science have a symbiotic relationship. Data engineers make data reliable and consistent for analysis, while data scientists require reliable data for data exploration, machine learning, and other analytical projects that involve vast data sets. They also construct data pipelines to make data available to data scientists. Data scientists typically rely on data engineers to find and prepare the data for their subsequent analysis. Data scientists use the data for analytics and other projects to improve business operations and results.

Data scientists and data engineers also differ in their focus and skill sets. Data engineers typically don’t have a specific focus; they’re usually competent in several areas and have well-rounded knowledge and skills. In comparison, data scientists usually embrace specialized areas of focus. Data scientists tackle new, big-picture problems, and data engineers put the pieces in place to make data scientists’ jobs possible.

Importance of Data Engineering

Data engineering is the initial data analysis and model-building step. Analysis can only be performed with well-organized data. Additionally, thanks to the development of Artificial Intelligence, access to good data has become more critical than ever. In summary, data engineering plays a significant role in:

Collecting and sourcing data from diverse sources. Data collection techniques include ethical web scraping and API calling.
Maintaining databases. Databases can vary based on the organization and include MySQL, Oracle, PostgreSQL, etc.
Preparing data for further analysis. This covers basic data cleaning and treatment steps performed by data engineers so that data analysts and scientists can perform further analysis.

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What Do Data Engineers Do?

Data engineering is in increasing demand. Data engineers are IT professionals who design systems that unify data and can help others navigate it. Data engineers perform many tasks like:

Acquisition. Finding and gathering disparate data sets.
Cleansing. Locating and cleaning any data errors.
Conversion. Changing all the data into a standard format.
Disambiguation. Interpreting data that may be interpreted in different ways.
Deduplication. Removing duplicate data copies.

Once these tasks are done, data can then be stored in a central repository like a data lake. Data engineers can also copy and move data subsets into a data warehouse.

Role of a Data Engineer

Although the precise data engineering responsibilities can differ by organization, typical often responsibilities include many of the following:

Building, testing, and maintaining database pipeline architecture
Creating data validation methods
Acquiring and cleaning data
Improving data reliability and quality
Preparing data for prescriptive and predictive modeling
Developing data set processes
Developing algorithms to make the data usable

Data engineers normally focus on collecting and preparing data for data scientists and analysts and typically are found in three primary roles:

Generalists. Data engineer generalists work on small teams, performing end-to-end data collection, intake, and processing. These engineers might possess a broader range of skills than typical data engineers but have less knowledge of systems architecture. The generalist role is ideal for data scientists wanting to enter data engineering.
Pipeline-centric engineers. These data engineers usually work on data analytics teams, tackling more complex data science projects across distributed systems. Mid -and large-sized organizations are more likely to need these engineers.
Database-centric engineers. These engineers implement, maintain, and populate analytics databases, typically found at larger companies where data is distributed across many databases. These engineers work with data pipelines, fine-tune databases for more efficient analysis, and develop table schemas by leveraging extract, transform, and load (ETL) methods. ETL copies data from different sources and places them into a single destination system.

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Necessary Data Engineering Skills

Data engineers are proficient in programming languages like C#, Java, Python, Ruby, R, Scala, and SQL, although primarily Python, R, and SQL.
Data engineers must be acquainted with Business intelligence (BI) platforms and how to configure them. They must also know how to work with interactive BI platform dashboards and establish connections with data lakes, data warehouses, and other data sources.
Engineers need a good grasp of ETL tools and representational state transfer-oriented APIs to create and manage data integration tasks. These skills also facilitate business users’ and data analysts’ access to prepared data sets.
Data engineers should understand data warehouses and data lakes and how they operate.
Data engineers must understand NoSQL databases and Apache Spark systems, which are becoming increasingly common in today’s data workflows. Additionally, data engineers should know about relational database systems such as MySQL and PostgreSQL. Finally, they should be acquainted with Lambda architecture, which supports unified data pipelines for real-time and batch processing.
Although machine learning (ML) is more the purview of data scientists or machine learning engineers, data engineers must also be familiar with its principles to prepare data suitable for machine learning platforms.
Finally, data engineers need to know Unix-based operating systems (OSes). Unix, Linux, and Solaris offer functionality and root access that other operating systems like Windows and macOS don’t. Unix-based operating systems give users more control over the OS, which can benefit data engineers.

Why Pursue a Career in Data Engineering?

Data engineering is an attractive career option because it’s in high demand thanks to the ever-growing amount of data generated today. This demand allows you to play a vital role in building systems that can collect, store, and process data, thus making it accessible for analysis and informed decision-making, translating into job security and a great career growth potential while requiring robust technical proficiency in programming and problem-solving to design and build scalable data pipelines and architectures.

Sounds great, doesn’t it? So, how do you become a data engineer?

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How to Become a Data Engineer

There are many options available for becoming a data engineer:

Get a degree. Useful degrees for hopeful data engineers include a bachelor’s in computer science, applied mathematics, physics, or engineering. If you want to improve your chances of getting hired, a master’s degree in computer science or engineering may help candidates set themselves apart from the rest of the job seekers.

Take online courses. Plenty of reasonably priced or free online courses are well suited for learning data engineering skills.

Build a portfolio. Build a portfolio centered around data engineering-related projects. Your portfolio shows recruiters and hiring managers the kind and quality of work you can produce. Independent or coursework projects can potentially show what data engineering knowledge you have, as well as problem-solving skills.

Engage in project-based learning. This is a more hands-on approach to learning data engineering skills. The initial step is setting a project goal and then determining which skills you need to achieve it.

Get an entry-level position. This option can be a paid, entry-level job or an unpaid internship. Obviously, it’s better to get the paying option, but in either case, you have your foot in the door and can now improve your skills as you build connections.

Data Engineering Tools

Data engineers have access to many different tools when working with data including:

ETL Tools: ETL (short for extract, transform, load) tools move data between systems. These tools access and extract the needed data, then apply rules to “transform” it through steps that make the data more conducive for analysis.
SQL: Structured Query Language (SQL) is normally used for querying relational databases.
Python: Python is a widely used, general programming language. Data engineers often choose Python for ETL tasks.
Cloud data storage: This includes Amazon S3, Azure Data Lake Storage (ADLS), and Google Cloud Storage, to name a few.
Query engines. As the name implies, these engines run queries against data to get answers. Data engineers often work with engines such as Dremio Sonar, Flink, and Spark.

So, what does the future hold for data engineering?

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Future Trends in Data Engineering

Unsurprisingly, artificial intelligence (AI) is poised to make an impact on data engineering. AI can handle repetitive tasks by decreasing the number of time-consuming processes. AI models can automate the data collection process. Additionally, AI models trained on big datasets can be used to find data errors, thereby simplifying the process of anomaly detection and data cleaning.

In the coming years, expect to find the following trends in data engineering:

Automated Data Pipelines: AI can automate data pipeline creation and maintenance, which are vital for taking data from different sources and moving it into a data lake.
Intelligent Data Governance: AI models may analyze data sources and use patterns to automatically ensure data quality and security.
Predictive Maintenance: Finally, AI can monitor and optimize data infrastructures and processes. AI models can predict potential failures by analyzing historical data and real-time performance metrics.

Are You Interested in a Career in Data Science?

The data science field offers unlimited potential and opportunities for aspiring IT professionals. If you’re considering a career in data science or want to boost your skill set, consider this data science bootcamp. This 11-month course teaches data science and generative AI skills while giving you experience in ChatGPT, DALL-E, Midjourney, and other well-known data science tools.

According to Glassdoor.com, data engineers earn an annual salary of around $105,000. So, if you’re looking for a well-paying, in-demand role in the IT world, start your journey by signing up for this valuable data science certification course.

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