Data engineering is strictly associated with data science and AI in general. What is modern data engineering? What part of the data engineer’s job can be automated? What can we expect in the near future? These are the questions that concern both people who want to pursue the data engineer’s career and people who manage data science projects in companies. Are we at the beginning of the end of data engineering as we know it today? Let’s find out.

Before we start talking about the future of modern data engineering, let’s think for a few moments about what this area of data science looks like today. Next, we will think about whether modern data engineering can be automated.

What is modern data engineering?

First off, to understand what the future of data engineering might look like, we have to examine how it looks today. In general, data engineering is a strictly technical part of data science. While data scientists try to analyze data they process and draw useful conclusions, data engineers are here to make it all work on the production and technical level. In other words, they are responsible for giving tools and means to data scientists so that they can do their job.

modern data engineering

This means that all the data-science-related tools and solutions, such as dashboards, visualization tools, and reports also lie within the area of data engineers’ interest. They build data platforms (the technological infrastructure that serves as a base for data scientists), maintain, and develop them according to the specific project.

A quick side note: Data platforms and their construction depend primarily on the type of project you run. The more advanced technologies are necessary for your project, the more extensive and complex data platforms are needed.

You may find it interesting – Data Science vs. Data Engineering

The role of data engineer

Usually, data engineers deal with three crucial data-related tasks:

  1. Extracting data: Modern data engineering starts with getting big data from various sources and inputting it into the data platform. These sources can comprise a whole spectrum of things, from CRMs up to call center logs. It all depends on the project you have to execute.
  2. Storing data: Data engineering deals with means to store data. There are two most popular IT solutions: Data warehouses and data lakes. In AI (and BI), data warehouses are far more prevalent because they are suitable when it comes to storing structured and organized data. Data lakes come in handy when you have diverse file formats, and at least some part of your data is unstructured.
  3. Transforming data: The last step is related to preparing data so that it’s useful from the project’s standpoint. Transforming data includes cleaning, structuring, and formatting the datasets. This process is necessary to analyze and process data in subsequent stages of work.

modern datasets

Modern data engineering specializations

Additionally, data engineering is not a limited field of expertise. It can change and adjust to the needs of the company. Data engineers are divided according to their specializations. This way, we can designate:

  1. General-role data engineers: That’s the most versatile specialization. They are usually found in small and medium-sized companies working partly as data scientists. And yes, general-role data engineers do a lot of data scientists’ work; they are responsible for the entire data flow.
  2. Warehouse-centric data engineers: Their role revolves almost exclusively around data warehouses. They build them, configure, maintain, and develop. Warehouse-centric DEs work with big data and integration tools.
  3. Pipeline-centric data engineers: They are somewhere in the middle between general-role and warehouse-centric data engineers. Their primary role is to integrate and connect data sources with a data warehouse. Therefore, they are frequently responsible for the ETL process as well.

modern data engineering specialization

So, how could we wrap up the question of modern data engineering? It’s a field that’s all about making data analytics work. It’s the backstage of data science, providing tools and solutions necessary to execute a given project. With that covered, the next thing you ought to be interested in is the demand for data engineers.

Are data engineers in demand?

This question is primarily asked by young people at the start of their careers or higher education. The good news is the answer is yes. Data engineers ARE in demand, and we have solid numbers to support this thesis.

In early 2020, there was published the Dice 2020 Tech Job Report: The Fastest Growing Hubs, Role and Skills. The authors of this report named data engineer as the fastest-growing job in technology in 2019, with a 50% year-over-year growth in the number of open positions. And it goes further:

“Demand for data engineers rose a respectable 45%, the report found, while demand for machine learning engineers was up 89%. Computer vision engineers (146%), search engineers (137%), and security engineers (49%) also have good job prospects in 2020, according to the report.”[1]

Engineering growthImage source: Datanami.com

Statistics

Of course, 45% is way behind the leader–AR/VR engineer, but it’s still a solid result indicating that there will be a lot of work for young data engineers in the near future. That’s because data-related and AI-related projects are more and more popular in almost every sector and industry. We can state with full confidence that today, almost every large enterprise is looking for a data engineer or will be looking for one shortly. And on top of that, we have AI companies like Addepto. We also hire data engineers, and modern data engineering is one of our major fields of interest.

According to another source, The 2021 Data Science Interview Report[2], data engineering interviews increased by 40% in the past year.

data engineers

And one more thing, the same report states that the demand for data science jobs (and remember, this includes data engineering) will increase by 38% over the next 10 years. This means that there will surely be a job for you in the coming years. You can only expect to have more and more work.

Is data engineering still a good career?

Again, the answer is yes. And there are three elements that we want to mention to support this answer. Typically, when someone asks whether X is a good career, they mean three more specific questions:

  • Will I be able to find a job in the coming years?
  • Is this profession difficult to master?
  • Will I make enough money?

modern data engineering growth

Now, when it comes to data engineering, the answers to all these three questions are nothing but optimistic. You already know the answer to the first question. Various sources say that the interest in data engineers grows and will grow for at least 10 years. Let’s focus on the second question–is this profession difficult to master? Of course, the answer is both yes and no. It all depends on your current knowledge and abilities. If you are good with databases–the answer is no. In fact, you need just three skills to become a successful data engineer:

Modern data engineering skills

According to the aforementioned report, currently, the vast majority of engineering roles require only three main types of skillsets: SQL, Python, and algorithms. Machine learning, probability, and statistics knowledge are usually not necessary.

Modern data engineering skillsImage source: Interviewquery.com

Of course, this does not mean that you can become a data engineer overnight. There is still a lot of work ahead, but data engineering is one of the easier ones to master compared to other AI-related jobs.

Salaries in data engineering

If you’re already thinking about working in data science, you surely understand that salaries in this field are typically above average. The same rule applies to data engineering. Let’s take a closer look at several sources:

  1. According to PayScale.com[3], the average data engineers’ base pay exceeds 92,400 USD per year. You can also count on various bonuses (PayScale estimates 2-16k per year) and profit shares (1-27k per year). That’s the most recent report on our list (updated May 2021).

salaries in data engineering

  1. According to Hired 2019 State of Software Engineers report[4], data engineer’s salary in New York in 2019 was around 132,000 USD per year.
  2. Glassdoor published their list of 50 Best Jobs in America for 2020[5]. A data engineer is in 6th place with an estimated salary of over 102,400 USD. At the time we’re writing this article, there were over 6,900 job openings for data engineers on Glassdoor.

So, no matter which source you choose, predictions are rather optimistic. Even a salary of 94,000 USD annually is not bad, and as the workforce shortage and demand grows, you can expect to make more money in the near future.

Will data engineers be automated?

To some extent, data engineers’ work can be automated, yes. Even today, we can talk about something called augmented analytics, where AI elements and algorithms are incorporated into every phase of the data analytics process. But what can AI really do when it comes to modern data engineering? There are several elements worth mentioning:

  • Recommend an optimal data model structure
  • Standardize data
  • Analyze ready-made data sets, primarily by making recommendations as to what to fix (e.g., redundant entries), implement active learning, or even fix these errors on their own
  • Support the ETL process

data engineering automation

When you look at this list of ways in which data engineering can be automated, what do you think? That data engineers are done, and soon enough, AI algorithms will build infrastructures and analyze data on their own? That’s unlikely. The role of automation in data engineering is quite simple – intelligent algorithms can take all the burdensome tasks off the data engineers’ shoulders. But that’s not the way to replace them! Think of AI analytics systems as a second set of eyes for the data engineering team, freeing them to focus on the challenges and tasks that require a human touch, are more complex, or simply drive more value.

The end of the translator profession?

It was the same story with the translator profession. When Google Translate emerged, some people foretold the end of the translator profession. And mind you, Google Translate was introduced back in 2006. Today, it’s 2021, and translators and translation agencies are still here, building the market worth about 56 billion USD[6].

Additionally, according to ReplacedByRobot.info, there is just a 3% chance of automation when it comes to modern data engineering.

So, if data engineers of the future are still humans, what can we say about the future of this profession?

The future of data engineering

There are four crucial trends and predictions that we want to share with you:

AUTOMATION

As we said earlier, the data engineer profession will most likely never be fully automated, but all the tedious and repetitive tasks can surely be automated and shortly will. The data engineers of the future will have more advanced intelligent tools at their disposal. As a result, their work will be accelerated and facilitated. As a result, they will be able to achieve more in a shorter time and focus exclusively on the most important jobs and assignments. Smart data tools will do everything that’s repetitive and straightforward.

SPECIALIZATION

Earlier in the text, we showed you three major specializations within modern data engineering. We can expect that this trend will grow, and data-related jobs will become separated and dispersed. There will be no such thing as a data scientist doing data engineer’s work in the near future. Moreover, we can expect to see data engineers split into backend and frontend teams. As a result, companies will execute more complex and extensive data projects involving many different specialists.

data statistics

UNIVERSALITY

Soon, data will be used extensively by almost every company. Data tools and warehouse solutions will become more affordable and easy to use, making data-driven management and innovations more popular across every sector and industry. We believe that shortly even SMEs will adopt data-based solutions and incorporate them into their decision-making processes. Again, this means more work for data engineers!

ORGANIZATION

Every day, the whole world generates quintillions of data. The majority of it still remains disorganized. In the near future, data documentation and data cataloging will become a front-burner issue. Companies will try to organize and catalog every information they possess in order to make their efforts even more effective. Unfortunately, today, we still have to deal with undocumented, uncleaned, and untested data, and it’s quickly outnumbering the data that’s actually used, analyzed, and understood. That’s definitely one of the main problems to solve.

ACCELERATION

With amounts of data growing at a mind-boggling pace, we need to devise new algorithms and ways to move and analyze data faster. That’s the last element that will shape the data engineering of the future. Current processes will have to be optimized, and some entirely new ways of analyzing data will have to be invented in the coming years.

To sum up

As you can see, data engineering is a promising field of expertise that will only grow in the coming years. Of course, there are several challenges along the way that modern data engineering teams have to deal with, but we can expect that in the near future, all of them will be dealt with. And what about automation? The vision of robots taking over the planet (so-called grey goo scenario) is as scary as it is unlikely.

database

Today, intelligent algorithms are used primarily to help us do our work more effectively and quickly. The same rule applies to modern data engineering. Data engineers have tools that help them do their job more efficiently, and supposedly there will be many more such tools in the future, but that’s it. The work of a data engineer can be only partly (but never fully) automated. And for you, aspiring data engineer, that’s a good thing! You will be able to focus on the most interesting and challenging parts of your work, while smart algorithms will deal with all the boring and tedious tasks that you don’t want to do anyway.

If you are interested in data engineering and would like to find out how this profession can help you with AI and data-related projects–the Addepto team is at your service! We are working with various aspects of data science literally every day. We will gladly help you implement data science and data engineering so that your company can make the most of the data you possess. Drop us a line to find out how!


[1] Alex Woodie, Datanami. Demand for Data Engineers Up 50%, Report Says. URL: https://www.datanami.com/2020/02/12/demand-for-data-engineers-up-50/. Accessed Jun 20, 2021
[2] Jay Feng. Interview Query Blog. The 2021 Data Science Interview Report. URL: https://www.interviewquery.com/blog-data-science-interview-report. Accessed Jun 20, 2021
[3] Payscale, Average Data Engineer Salary. URL: https://www.payscale.com/research/US/Job=Data_Engineer/Salary. Accessed Jun 20, 2021
[4] HIRED, 2019 State of Software Engineers. URL: http://pages.hired.email/rs/289-SIY-439/images/2019-State-of-SoftwareEngineers-Report.pdf. Accessed Jun 2021
[5] Glassdoor. 50 Best Jobs in America for 2020. URL: https://www.glassdoor.com/List/Best-Jobs-in-America-2020-LST_KQ0,25.html. Accessed Jun 20, 2021
[6] Statista.com. Market size of the global language services industry from 2009 to 2021. URL: https://www.statista.com/statistics/257656/size-of-the-global-language-services-market/. Accessed Jun 20, 2021

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