How Do I Start A Career In Data Engineering

Are you truly excited to work with data and generate meaningful insight from a pile of nothing? Lucky for you – today, there are more open positions for Data Engineers and fewer professionals to fill them in. With that said, we’ll be going in-depth to answer the burning question – “how do I start a career in Data Engineering?”

Before hopping on to career-specific advice, let’s introduce the field of Data Engineering to those who’re unaware of it. Here’s a quick introduction of what you can expect in a Data Engineering role:

What is Data Engineering?

If you’ve been introduced to the Software Development process before – you’re likely aware of the pipeline between developers and operations. Consider the same analogy being applied to the Data Science world. Before Data Scientists can perform their analysis, the data has to go through a series of steps before it’s ready to be used – this is exactly where Data Engineers come in.

Data Engineering is practically the task of establishing an infrastructure which helps Data Scientists analyze data and build models. This infrastructure deals with the acquisition, storage, and processing of data adhering to the clients’ or business’ objectives. 

Now if you’re aware of the Data Scientist role; how do these two differ? As a Data Engineer, you’re focusing on producing data workflows e.g., acquiring data from a CRM solution and storing it after processing for easy analysis. Whereas a Data Scientist is tasked with manipulation of the data itself i.e., analyzing it for trends and analytics. 

What is an Entry-level Data Engineer’s Salary?

According to PayScale, careers in Data Engineering can pay up to $92,198 a year on average. If we shift out of averages, the base salary ranges from $65,000 and escalates up to $132,000 based on skill and experience. Based on the submission of 296 salaries to PayScale, the average figure for an entry-level data engineer salary was close to $77,000.

You might also notice – an entry-level Data Engineer salary is still a higher  figure when compared to other Engineering roles. Although it falls short of a Data Scientist’s salary, it is still worthwhile to pursue careers in Data Engineering. Still wondering, how do I start a career in Data Engineering? We’re almost there!

How to Become a Data Engineer?

If we look at  our definition of a Data Engineer’s role; it’s mainly responsible for developing strong data workflows and infrastructures. If we further break it down – it could be having the knowledge of:

  • Advanced Programming (to script, develop, and maintain infrastructure)
  • Distributed Systems (to build strong data acquisition workflows)
  • Data Pipelines (managing the flow of data from one system to another for analysis)

Now let’s switch back to how to become a data engineer. The general roadmap on starting a career in Data Engineering can be plotted as a KCE methodology

  1. Knowledge: Acquire the skills to become a data engineer
  2. Certifications: Get an undergraduate degree (or) equivalent certificates Expertise: Solving engineering problems

Once you have completed the KCE process,  Applying to entry-level jobs 

The role is quite technical and it is recommended that you have a computer science or applied mathematics background. Not only will this knowledge help you out, but you’ll also have an easier time understanding the core concepts of data engineering. 

Although employers are slowly shifting to degree-less employment criteria, it’s still quite far away. If you don’t have a degree in Sciences or Maths, you should have valuable certifications in programming, databases, or cloud. This way you’re compensating for the lack of an official degree.

Let’s shift our attention to the skills required for data engineers.

What Skills Are Required for Data Engineer?

For entry-level careers in Data Engineering, you’ll still be required to possess knowledge in several technical domains. Below, we’ve summarized the skillset that might nudge you along the way in acing your first interview for an engineer: 

1. Programming

Programming is the first basic requirement in becoming a Data Engineer. You should invest your time in Java, Scala, Python, and C++ which are at the forefront for developing large systems in the most efficient manner. For example, you might be tasked with writing a processing algorithm to analyze and process large data sets. What’s the best language to do so? That’s your expertise.

2. Databases

Data Engineers are proficient in both SQL and no-SQL databases. Although data manipulation doesn’t always fall under the scope of an engineer’s work, you’ll be tasked with architecting scalable databases. Since corporate warehousing solutions have petabytes worth of data, maintenance of these large-scale systems becomes tedious and requires extensive knowledge of database administration.

3. Cloud Infrastructure

Physical infrastructure is slowly withering away and data is shifting to the cloud. Sound knowledge of cloud computing is a must-have in other to store and process large amounts of data on the cloud. For example, AWS is one of the largest cloud providers supporting Big Data followed by GCP and Azure.

4. Data Warehousing

Whereas a standard database can only hold limited data, warehouses store huge volumes of historical data for querying and analysis. A company’s active assets which produce even a bit of data are likely connected to the warehouse to support data storage.

5. Machine Learning

Though you won’t always be using machine learning in your day-to-day job, it can massively help you acquire, sort, and process data swiftly. Not to mention, these algorithms are trained by large data-sets, something a Data Engineer is closely linked to.

How Do I Start a Career in Data Engineering?

Although we’ve summarized several key points in “How do I start a career in Data Engineering”, we can further break down the roadmap into achievable steps. Whether or not you’re a certified professional or a graduated fellow, these steps are generic and can apply to anyone.

How do I start a career in data engineering Here’s our quick guide on starting your career in Data Engineering the right away:

  1. Pick a Cloud Service and Sign Up for Trial

Firstly, pick your favorite cloud service. We recommend going for AWS since it’s the most widely accepted cloud provider and the services community support as well. Say you’ve picked up AWS, you can sign up for their free trial to test out their big data, data analysis, and database tools. GCP and Azure offer free trials as well; you can easily shift to those as well.

  1. Learn the Fundamentals of Cloud Using Tutorials

To get started with the cloud, we recommend using well-researched tutorials. Every cloud provider has extensive documentation which can help you know more about the service you’re interested in. For example, Amazon Redshift is a cloud-native data warehouse with loads of documentation online. Other than that, you can also explore content creators on YouTube to gain fundamental skills. Otherwise, refer to the next point.

  1. Consider Joining a Class to Learn Fundamentals

You can also join classes – whether online, in-person, or self-paced – to learn the required skills for cloud and programming. For online and self-paced courses, we recommend signing up with MLAcademy. They have one of the largest libraries to learn cloud computing with practice. For in-person classes, you might want to check out local computing trainers and see if they offer any classes on AWS or other cloud providers – especially for data engineers.

  1. Pick a Data Engineering Problem and Solve It on Cloud

Once you’ve gained considerable knowledge, it’s time to put it to good use. Data Engineering encompasses several problems and developing solutions to solve them most effectively. Not only will applying your engineering skills on data sets help you map out problems in the data, but it will also hone your processing skills. Kaggle is one of the best resources available to acquire public datasets on a wide range of problems. 

If you acquire the datasets, you can further practice your cloud computation skills by setting up your data workflows on the cloud. AWS has a series of tools for data engineering students such as AWS Lake Formation, Hadoop clusters, Spark, and Redshift. You can also compete in competitions on Kaggle and see your rankings in comparison to other engineers.

Conclusion

We hope we’ve answered your queries on common questions like “how do I start a career in data engineering” or “what skills are required for data engineers”. Data Engineering is a fairly new field in the Data Science realm but is quickly growing. If you love working with data and solving real-world problems, several careers in Data Engineering await you.

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