Data engineering is a rapidly growing field, and there’s a high demand for expert data engineers. If you might be an information scientist, you might be wondering should you can transition into data engineering. The excellent news is that there are a lot of skills that data scientists have already got which might be transferable to data engineering. In this blog post, we are going to discuss how you may develop into an information engineer should you are an information scientist.

But first, let’s briefly define what an information engineer is.

What is a Data Engineer?

This individual is accountable for constructing and maintaining the infrastructure that stores and processes data; the kinds of knowledge could be diverse, but mostly it’s going to be structured and unstructured data. But they’re not lone rangers, type of speak. Data engineers may also work with data scientists to design and implement data pipelines; ensuring regular flows and minimal issues for data teams. 

They’ll also work with software engineers to be certain that the information infrastructure is scalable and reliable. These professionals will work with their colleagues to be certain that data is accessible, with proper access.

How to Become a Data Engineer

For the information scientist who’s trying to make the transition, that is the million-dollar query, and should you search online, you’ll get one million answers, but the reality is that there are just a few steps that you may take to develop into an information engineer. So let’s undergo each the 1st step by one, and show you how to construct a roadmap toward becoming an information engineer.

Identify your existing data science strengths. 

Like with any skilled shift, it’s at all times good practice to take inventory of your existing data science strengths. Data scientists typically have strong skills in areas akin to Python, R, statistics, machine learning, and data evaluation. Believe it or not, these skills are invaluable in data engineering for data wrangling, model deployment, and understanding data pipelines. With that said, each skill could also be utilized in a special manner.

For example, should you’re a talented Python programmer, there could also be other packages, libraries, and frameworks that you just are conversant in. This skill can easily be transferred with you as you develop into an information engineer, but will likely see different uses. So be aware of what you may do, and see how your skills can relate.

Identify your data engineering gaps. 

The truth is, you’ll have some amazing skills that you may transfer over, but at the top of the day, data scientists and data engineers are two completely different professions. So as you’re taking inventory of your existing skill set, you’ll want to start out to discover the areas where you’ll want to give attention to to develop into an information engineer. These areas may include SQL, database design, data warehousing, distributed systems, cloud platforms (AWS, Azure, GCP), and data pipelines.

Learn more concerning the cloud. 

One thing that may’t be stressed enough is that working on cloud/hybrid platforms will develop into a critical element of what you do. Data engineers must be conversant in the various cloud platforms and how you can use them to store and process data. This will develop into much more necessary as teams develop into more global with distant work becoming a mainstay in multiple industries. 

This implies that not only do the right infrastructures must be created, and maintained, but data engineers might be on the forefront of knowledge governance and access to be certain that no outside actors or black hats gain access which could spell compliance doom for any company.

Thankfully, any of those platforms offer sandbox accounts and free learning material so you may get your feet wet. Microsoft Azure specifically allows users to explore the Azure ecosystem and provides on-site training for users of all levels. With that said, many also offer industry-recognized certifications on their brand platforms. 

ETL (Extract, Transform, Load)

This is a core data engineering process for moving data from a number of sources to a destination, typically an information warehouse or data lake. ETL tools and techniques are used to extract data from quite a lot of sources, transform the information right into a consistent format, and cargo the information into the destination.

The reason that is a vital skill is that ETL is a critical process for data warehousing and business intelligence. It allows organizations to consolidate data from disparate sources, clean and prepare the information for evaluation, and make the information available for reporting and decision-making. Currently, there are a number of ETL tools available, including business tools, open-source tools, and custom-built tools.  

Stay on top of knowledge engineering trends. 

Another yet simpler tactic is by attending conferences. There you may network with fellow data professionals, try what’s causing the earth to shake in data engineering, and even meet up with data engineering thought leaders who’re trailblazing the industry.

Get more training

Clearly, this likely doesn’t should be said, however it’s going to be said since it’s necessary. Get more training! Data Science is currently reshaping how the world works, and for this reason recent tools, models, frameworks, packages, and theories are being born at a rapid pace. So staying within the know, and applying your skills in recent ways will keep you ahead of the pack. 

Now there are several ways to get more training in data engineering. You can take online courses, attend workshops, or enroll in a data engineering bootcamp. These could be done virtually, in person, or using a hybrid approach. But as was mentioned above, be sure that the training you’re receiving compliments your data engineering goals. For example, if you’ll want to get your A game with cloud platforms, then taking a bootcamp on R, isn’t going to chop it. So take inventory and take charge.

The upcoming Data Engineering Summit, colocated with ODSC East, will give you the option to show you all of those data engineering skills and more!

Connect with other data engineers. 

Though it’s been alluded to within the blog, it’s price having as its own section. Networking with other data engineers is a terrific solution to learn more concerning the field and get advice in your profession. Does it take time and energy? Yes! But it’s top-of-the-line investments you may make as knowledgeable. As you construct a top quality network, not only are you constructing lines of communications that may keep you up so far with the newest in data engineering, but you’ll even have an interpersonal web of other individuals who know you, and if required, can go to bat for you if needed.

So how are networks built? Well these days, you could have several methods. First, you may connect with data engineers on LinkedIn. LinkedIn is a terrific platform to get to know people inside the field and get some great insights on what’s happening. There are also meetups specific to data engineering But the king of networking remains to be the conference. By attending conferences you’ll not only get some great face time together with your peers, but you’ll also give you the option to map out how you can achieve your goals as knowledgeable.


If you might be an information scientist, you could have the abilities and knowledge to develop into an information engineer. By following the steps on this blog post, you may transition into data engineering and begin a brand new and exciting profession.

And as any aspiring data engineering skilled knows, the very best solution to stay ahead of the curve is by maintaining with the newest in all things related to data and data engineering. The best solution to do this is by joining us at ODSC’s Data Engineering Summit and ODSC East. 

At the ODSC Data Engineering Summit on April twenty fourth, you’ll be on the forefront of all the foremost changes coming before it hits. So get your pass today, and keep yourself ahead of the curve.

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