Becoming a Data Engineer – all you need to know
For Developers

February 15, 2024

Becoming a Data Engineer – all you need to know

Introducing - Data Engineer 

If there's a lesson to be learned from digital transformation, it's that the pace at which the technology is developing continues to intensify with every passing day. The thing is - the volume of produced data creates storage issues, and proper data management requires as much optimization as possible. Companies store their data in a variety of forms - documents, tables, graphs, media files - you name it. Things get convoluted and chaotic, so new and more robust platforms were designed to streamline the process. It created an acute need for qualified specialists with expertise in accumulating and storing all that data, ensuring their compliance with user needs and requirements. That's how Data Engineers were born.

While the demand for Data Engineers is at an all-time high (the number of interviews for this position has increased by a staggering 40% over 2020 alone), the market is still experiencing a drastic lack of specialists. Why? Let's delve a bit deeper to find out. One thing is certain - be it telcos or banking institutions, retail or marketing, everyone and their mum are literally hunting Data Engineers who would bring some order to their data management. 

What Do Data Engineers Do

So what makes Data Engineers so special in the first place? Simply put, it's a high-level programming and information engineering specialist laser-focused on extracting, processing, and organizing data. From customer databases to AI algorithms, any company or project features its own data sets that need to be moved and transformed with the help of software solutions and a processing environment. A Data Engineer's goal is to write code simplifying all data transformation stages. It doesn't end there - Data Engineers use special scripts to clear data arrays of any occurring errors and repetitions and accelerate automatic execution. 

Data Engineers give companies an upper hand, as optimized data speeds up business processes, resulting in greater efficiency and productivity. Granted, data engineering tasks differ from company to company. Some want to program new data management solutions, while others are focused on modernizing the existing ones. In some cases, Data Engineers collaborate with Data Scientists. Though their responsibilities are closely related, the final say in the success of the project still lies on the shoulders of the Data Engineer. Still, the division of labor does speed the process up.

The 3 Major Roles of a Data Engineer 

Though Data Engineers may niche down in line with the company-specific needs, their roles may broadly fall under one of the following categories:

  • Generalist. In some cases (most cases), generalists are the only professionals who focus on data management. That's how they end up being involved in every aspect of data processing and analysis. Consequently, a Data Engineer is making part of a smaller team, collaborating with a Data Analyst or a Data Scientist. 
  • Pipeline-centric. These Data Engineers dwell in larger companies. Since these companies have bigger needs, they tend to pair their Data Engineers with Data Scientists to ensure the division of labor and allow the engineers to focus on computer science and amp up their distribution systems expertise.
  • Database-centric. As the name suggests, these Data Engineers specialize in database analysis. Their job is to spread across multiple databases, making dataflow management their full-time job. Database-centric Engineers work in larger companies where a vast data array demands undivided attention. 

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Data Engineer Skills and Responsibilities 

Due to the shortage of Data Engineers on the market, such experts need to have universal expertise in every aspect of the job. From managing databases to programming to data analytics, a Data Engineer needs to be able to switch between these tasks to deliver the expected results. An analytical mindset is required to master this job, and the ability to think logically goes hand in hand with it.

A Data Engineer's skillset needs to be quite broad and must include but not be limited to the following expertise:

  • The ability to write complex queries, with a deep understanding of SQL;
  • Understanding of basic ETL tools;
  • Understanding of ClickHouse and PostgreSQL database solutions;
  • Python, Java, Scala – a Data Engineer needs to be proficient in programming languages;
  • Basic understanding of Tableau or Power BI dashboards;
  • The ability to work with Hadoop tools;
  • Understanding of major cloud platforms;
  • Ability to use Docker to run ready-made services;
  • Automating internal processes using GitLab functions;
  • Understanding of both Pandas and NumPy libraries;
  • Ability to write new scripts from scratch.
  • As for the responsibilities the job entails, a Data Engineer needs to know how to handle the following assignments:
  • Extract valuable data from various raw sources;
  • Convert and transform useful data;
  • Process data using machine learning elements;
  • Design dashboards to keep track of necessary data elements;
  • Deploy software solutions that store data on a server or in the cloud;
  • Devise systems and alerts to monitor and prevent errors;
  • Accelerate data processing;
  • Carry out A/B testing for validating performed tasks;
  • Create frameworks for data analysis.

Data Engineer Certifications

We've established some of the primary skills and responsibilities of a Data Engineer, so let's try and answer the question that matters the most – how can you get the expertise necessary to become a Data Engineer in the first place? Granted, the profession requires a more adaptable approach to learning in that you need to be flexible enough to take on hybrid education where no one detail is less important than the other. Thus, completing specialized training courses to get relevant certificates would quadruple your chances of finding a well-paid Data Engineer job. These certificates serve as proof of mastery, and we've decided to list the more peculiar ones that could help you succeed:

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Data Engineer Salaries

According to DICE’s 2020 Tech Job Report, Data Engineer job postings had “the most significant year-over-year growth.” Such tendency is only expected to grow as more companies realize they need qualified Data Engineering specialists able to navigate through large data arrays. For the job market, it means that the number of Data Engineering vacancies will continue to increase by the day. 

Glassdoor indicates that the average Data Engineer in the US earns around $113K per year. On the other hand, Ziprecruiter shows a range between about $53K and $185K per year. Though the level of seniority plays a major role in impacting the salary, one's location is also a thing to consider as Data Engineers from Bay Area (San Francisco in particular) earn about $125K per year on average.

As for the EU, the average European wage sums up to about €67K, and there's also a significant difference in salaries from country to country. For instance, Data Engineering salary is the lowest in Spain and Italy (€38K and €33K accordingly) while Switzerland and Scandinavian countries offer the highest-paying Data Engineering jobs in Europe (€65K to €113K). 

You may be wondering what’s the biggest you could earn as a Data Engineer? The short answer – the sky’s the limit. For instance, the TOP tech giants offer generous reimbursements for those willing to join their ranks as Data Engineers:

  • Netflix. The streaming giant pays handsomely and is ready to offer up to $295K per year to its Data Engineering specialists.
  • eBay. One of the largest online marketplaces in the world will pay up to $179K per year to in-house Data Engineers.
  • Meta. The multi-billion conglomerate, home of Facebook, Instagram, and WhatsApp, offers about $175K per year to its Data Engineers.
  • System Soft Technologies. The IT giant welcomes Data Engineers wholeheartedly and offers up to $171K per year for their services.
  • Capital One. The banking organization is in need of qualified Data Engineers and will reimburse them with up to $167K per year. 

Data Engineer – Entry-level Opportunities

When opting for this in-demand career, the first thing you need to consider would be learning more about Data Algorithms and how they work. You can start with a bunch of free courses available on Coursera. These will allow you to learn all the basic pipeline building concepts and ETL processes that help transfer data from one place to another and understand the cloud storage systems and their applications.

Suppose you already have a basic understanding of this niche. In that case, you can use the Data Engineering roadmap to pinpoint the aspects of the job that are less familiar to you and focus on determining your weak points to master relevant new skills and improve your chances of succeeding.

Entry-level Data Engineering Jobs

Though a Data Engineer is required to demonstrate a sufficient skillset, the low level of competition in this field gives a chance to occupy this niche even as a novice specialist. The demand for Data Engineers of any seniority level is skyrocketing. Hence, unlike other IT specialists, even junior Data Engineer jobs pay handsomely and are easy to find. An entry-level Data Engineer's salary often surpasses some of the other senior developers' paygrade, so you can apply for a job with little to no experience in the field.

Glassdoor lists dozens of entry-level Data Engineering vacancies with paygrade ranging from $46K to $107K per year, while Ziprecruiter offers Junior Data Engineer opportunities with an average salary of $75K per year.

Major companies are more than willing to invest in entry-level Data Engineers. For example, Visa is looking for College Undergrads, offering $90K per year as a solid and highly motivating reimbursement. IBM is not far behind with an entry-level Data Engineering position that pays $70K a year and invaluable experience of working in one of the biggest and most established tech companies on the planet. 

See, even an entry-level Data Engineer may start bringing actual value to the company within 3-4 weeks after onboarding. To succeed in this entry-level role, you need to be good at SQL queries, know one of the programming languages (Java, Python, etc.) and familiarize yourself with development tools, such as Docker, Jenkins, or Github. It's important to know a thing or two about cloud infrastructure (Azure, GCP, AWS, etc.) and the Agile essentials to streamline the development process.

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Data Engineering Internship

If you’ve mastered the fundamental skills we’ve listed above, your chances of landing a Data Engineering internship are pretty solid. Tech giants, such as Amazon, are always happy to foster recruits with decent reimbursement and productive environments that will reinforce your chances of becoming an industry-leading professional. The same Amazon offers $7,7K a month to its Data Engineering interns and provides career opportunities in this field. 

Data Engineering interns are assigned to in-house Data Engineers to help with their daily tasks and, consequently, accumulate the expertise required to fill their shoes full-time. Such internships may last anywhere between a few months and a full year. During that time, the interns will be busy collecting data, integrating large data arrays in daily operations, and tracking the efficiency of their work via various KPIs. 


Though the profession of Data Engineer is somewhat challenging, it's one of the more exciting career prospects and a great way to occupy an in-demand niche. Thanks to the fact that the competition is still very low, you have the time to master this career through courses, paid internship programs, and junior-level job offerings that are easy to find on the market. Sure, it requires some time, but it will pay off very soon, and you will have a possibility to jumpstart a stable and highly profitable career from the get-go.

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