Data Engineer – Who is it, what does a data engineer do, and an overview of the profession

Imagine a huge warehouse where tons of goods—boxes, crates, bags—are delivered every day. All of this needs to be sorted, checked for defects, and neatly stacked on shelves so that the right product can quickly reach the customer. The world of data works much the same way. Only instead of boxes, there are numbers, text, user clicks, transactions, and messages. And the hero of this article is responsible for bringing order to this world. In this article, we explain what a data engineer is and what they need to know and be able to do when processing data.

Who is this? 

In today’s world, we’re surrounded by a huge flow of information: online purchases, video views, social media messages, data from sensors in smart homes, and even cars. All of this represents terabytes of data, which by itself means little until it’s prepared for analysis. It’s like scattered puzzle pieces. Someone is needed to help assemble them into a coherent picture so businesses can make the right decisions, create new products, and improve services.

A data engineer is an IT specialist who designs, builds, and maintains systems for collecting, storing, and processing large volumes of information. Their job is to ensure that information from various sources is delivered to key employees in a convenient and structured format.

What does a data engineer do?

Let’s say you have a food delivery app. Customers place orders, couriers deliver them, and restaurants prepare them. All these actions leave a digital trail: what was ordered, when, where there were delays, and how much the delivery cost. Statistics come from multiple sources and in different forms. It needs to be prepared so that managers can understand in which areas couriers are frequently late or which dishes have become popular this week.

The hero of our article is engaged in the following:

• Collects information from various sources, including websites, apps, databases, sensors in production, or terminals in stores.

• Cleans data. For example, if the database indicates that a person was born in 1799 and also has a phone number of “123,” the engineer will not allow such “dirty” data.

• Stores information so it can be quickly accessed. It’s like a warehouse where items aren’t piled up in one place, but organized so the right drawer can be found in seconds.

• It transmits the results to analysts and data scientists, who use them to create reports, forecasts, or train neural networks.

Data engineering is about creating an infrastructure that transforms disparate streams of diverse facts into a robust system for analysis. If you want to work with information at a deep level and create a true foundation for decisions, data engineering can be an excellent choice.

Differences from a Data Scientist

These two professions are often confused, as both work with metrics. However, these specialists have different roles, tools, and goals. The former is responsible for structure and reliability, while the latter is responsible for analysis and meaning. While the former builds the foundations, the latter rides them and opens new horizons. We’ve compiled a comparison chart to clearly explain the differences.

Where is a data engineer needed?

Telecom, banks, retail

Every call, payment, or purchase leaves a digital trace. And there are millions of such traces. All of this needs to be collected, filtered, and passed on. For example, to understand customer behavior and improve services.

Online services and advertising

Here, it’s crucial to understand user behavior: what they search for, what they click on, where they get lost. Technical processes need to be streamlined so marketers can launch accurate and timely campaigns.

Logistics and transport

Optimal routes, on-time delivery, and avoiding traffic jams—all of this requires taking into account a huge number of parameters in real time. And here, a specialist ensures that all necessary signals are processed without delay.

Gaming industry

When millions of people play, you need to understand how they behave and where glitches occur. Engineers help build the infrastructure that monitors player activity and responds to it.

Government agencies and smart cities

Traffic management, security, and emergency forecasting—all of this relies on digital information, which must be managed effectively. And here again, data engineers play a key role.

Medicine and pharmaceuticals

Modern clinics and laboratories interact with a huge number of images, indicators, and test results. A specialist helps doctors quickly find what they need and make accurate decisions.

Agro-industry

Machinery in the fields, weather sensors, satellite imagery—all of this allows farmers to increase yields. This requires an expert to transform this information into useful recommendations.

Any company that wants to grow and develop in the market needs such specialists.

Sometimes, in small teams, one person combines both engineering and analytical tasks. But as the business grows, these roles become clearly separated—the engineer becomes the one who provides others with a solid foundation of knowledge.

How much is this in demand?

Today, Database and Data Engineers are among the most highly sought-after and well-compensated IT professionals in the United States. Demand for these specialists is projected to climb steadily over the coming years, fueled by exponential data growth and the widespread corporate shift toward data-driven decision-making. Here is why their expertise is so critical.

1. Data as a Core Corporate Asset

Companies increasingly recognize that information is far more than just numbers on a spreadsheet—it is a vital business asset. Extracting actionable value from this data requires robust, efficient systems for collection, storage, and processing. Engineers build this essential foundation, enabling accurate business intelligence and strategic decision-making.

2. Infrastructure Modernization and Tech Migration

As organizations transition away from legacy on-premise systems and navigate the complexities of multi-cloud environments (such as AWS, Azure, and Google Cloud) or open-source alternatives, infrastructure must often be re-engineered from the ground up. This massive wave of modernization creates a continuous need for skilled professionals to manage the transition.

3. A Widening Talent Shortage

The demand for qualified professionals consistently outpaces the available talent supply. This shortage is particularly acute in major tech hubs and metropolitan areas—such as Silicon Valley, New York, Seattle, and Austin. While job openings are abundant, finding highly qualified specialists remains a significant challenge for employers.

4. Rapid Technological Evolution

Continuous advancements in technology are reshaping the profession. The rapid rise of real-time data streaming, AI-driven data pipelines, automated cleansing, and advanced cloud architectures requires a new breed of engineer. Organizations need specialists who do more than just write basic code; they need architects capable of designing smart, adaptable, and scalable data ecosystems.

How much does a data engineer earn?

Even if you are just starting, the earning potential in data engineering is significantly higher than in many other IT professions.

Junior (Entry-Level)

Newcomers to the field (with up to a year of experience) can typically expect a starting salary of $70,000 to $90,000 in smaller regional markets, while starting offers in major metropolitan areas routinely exceed $100,000. The average entry-level salary ranges from $80,000 to $105,000, with highly competitive starting offers reaching up to $120,000. This provides an exceptionally strong foundation for career entry and future advancement.

Mid-Level (2–3 Years of Experience)

Mid-level specialists typically command base salaries between $115,000 and $150,000. At top-tier tech companies and major enterprises, mid-career compensation often climbs to $160,000 to $180,000.

Senior

Engineers with extensive experience and deep technical expertise generally start around $150,000. In large enterprises, base income averages $170,000 to $200,000, while elite specialists at major tech firms can see total compensation packages reaching $250,000 to $350,000+ when accounting for equity and bonuses.

Key Factors Influencing Your Offer

The final compensation figure is directly shaped by several critical factors:

Location: Salaries in major tech hubs like San Francisco, New York, Seattle, and Austin are substantially higher than in smaller regional markets or rural areas.
Experience: The maturity of your career and your proven track record with complex data pipelines directly correlate with your market value to employers.
In-Demand Expertise: Specializing in cutting-edge cloud infrastructure (AWS, Azure, GCP), real-time streaming architectures, and advanced AI-driven integration tools heavily skews salary offers in your favor.
The Employer: Major technology giants and leading financial institutions (e.g., FAANG companies, top-tier fintech firms) consistently pay well above standard market rates to secure premium talent.

What a Data Engineer Should Know and Be Able to Do

Programming and automation

The most popular language is Python, but Java and Scala are also frequently used, and Go or automation scripts (bash, PowerShell) may be useful for some projects. Automating routine tasks is an important part of the job.

SQL 

A solid command of the Structured Query Language is essential. You need to be able to work with relational databases (such as PostgreSQL or MySQL) and more specialized ones, like ClickHouse. A good engineer knows when it’s best to use each database and how to optimize it all.

ETL and orchestrators

One of the key tasks is building ETL processes: extracting information, transforming it, and loading it into the processing center. Airflow, NiFi, Talend, and other orchestrators are often used for this. It’s like setting up logistics—except for information flows, not goods.

Architecture and Big Data

It’s important to understand how a data warehouse or data lake works and how large distributed systems like Hadoop, Spark, or Kafka operate. It’s also important to be able to build and maintain pipelines—information processing flows that operate automatically and reliably.

Clouds and infrastructure

Modern projects most often live in the cloud—AWS, Google Cloud, or Azure. It’s important to understand how to set up cloud infrastructure and how it differs from on-premises servers. It’s crucial to be able to choose the right platform for your project’s needs.

Optimization and security

A good specialist not only builds systems but also ensures they operate quickly, reliably, and securely. They must be able to monitor performance, identify bottlenecks, and protect information from leaks and unauthorized access.

Theory is also important.

Although a data engineer is primarily a practitioner, basic knowledge of algorithms, data structures, and mathematics (linear algebra, probability) is essential. This is necessary to understand how to optimize systems and collaborate with ML teams.

A valuable specialist knows how to process large data flows, writes code, understands storage systems and clouds, configures pipelines, and monitors speed and security. And most importantly, they can turn a technical challenge into tangible business benefits.

Where do data engineers come from?

• From analytics outside of IT. People working with spreadsheets and reports often want to automate routine tasks and improve their data management, so they move into data engineering to master programming and building data processing systems.
• From analytics to IT. Professionals already familiar with basic Python programming and data science experience are looking to develop in a more technical direction, mastering ETL processes, database management, and Big Data technologies.
• From programming and development. Data engineers are often developers and programmers who want to shift their focus to working with data, building infrastructure, and automating processes. The reverse transition is also possible—from data engineers to data scientists or DevOps engineers, depending on interests and career goals.

• After graduating from a technical university. Faculties of IT, applied mathematics, and computer science. But theory isn’t enough—you need to improve your practical skills.

• Through courses and self-study. Online programs, pet projects, internships—anything where you can build real pipelines and practice.

People rarely enter this profession from scratch. These are often professionals transitioning from analytics and development who want to deepen their technical skills and tackle more complex and large-scale projects. 

Pros and cons 

This profession is suitable for those who enjoy order, technical challenges, and IT infrastructure, and want to be part of systemic change. However, regular training and a willingness to handle a variety of routine tasks are required.

How to become a data engineer and where to study

The “Data Engineer from Scratch” course from 365education is a great starting point. If you’re already working with data or just want to get into this field, this is the course for you. This course will be especially useful for:

• data analysts who want to gain a deeper understanding of the infrastructure;
• data engineers who want to systematize knowledge;
• BI developers who need to understand where their data comes from;
• Backend developers interested in moving into data engineering.

The program offers maximum practical training: you’ll build your own project, replicating a real ETL process for a major platform. Airflow, Spark, S3, and Greenplum—everything is covered, just like in production. The project is allotted two weeks: no other disciplines will distract you during this time. It’s all about working with infrastructure and real-world scenarios.

How to get started

Remember!

• You don’t have to be a mathematical genius. The key is logic, accuracy, and an interest in data.
• You can transfer from another field. The main thing is the desire to learn.
• It’s okay if you don’t know all the tools right away. You’ll grow as you go. The key is to get started.

Let’s sum it up

We’ve explained in simple terms who a data engineer is and what they do, how this profession differs from a data science engineer, and why database engineering is needed. It’s one of the most promising roles in IT: high salaries, stable demand, and work that impacts businesses in various fields—from medicine to game development.