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Data Engineer

Also goes by: Quantitative Data Engineer, Search Engineer, Technical Architect, Big Data Analyst, Solutions Architect, Data Warehouse Engineer, Data Science Software Engineer,

What Is a Data Engineer?

Data Engineers design, build, and manage data processing systems and perform batch/stream processing, ETL tasks, etc.

How much will I make?

Salaries can range by location and years of experience, but these are averages for the US.

according to Salary.com

Will I get a job?

Projected job growth is 37% for the period 2020-2030 in the US, according to the U.S. Bureau of Labor Statistics.

Total Openings
according to comptia.org

Who will I work for?

  • Tech Companies
  • E-commerce Companies
  • Financial Institutions
  • Consulting Firms
  • Data Analytics Companies

Data Engineer’s Daily Activities

No Data Engineer works alone! Data Engineers spend their days collaborating with designers, other developers, and product or project managers to bring data processing systems to life. Below you will get a sense for what a day-in-the-life of a Data Engineer could be:

Collaborate With Your Team Members to Build data processing systems

Data Engineers work hand-in-hand with their team members to create data processing systems. In order to do this, you’ll need to have an understanding of a wide variety of skills including Python, Java, SQL. Collaboration can take many forms, including planning and strategy meetings, design brainstorms, reviews, and pairing.

Code Your data processing system

Much of a Data Engineer’s day is spent coding. In practice this means having a development environment set up on one’s computer that allows you to track your progress as you go.

Test Your data processing system

One of the joys of working as a Data Engineer is that data processing systems are ALWAYS breaking! As a Data Engineer one of your core duties is testing your data processing systems for bugs and errors and working to fix them

How To Become a

Data Engineer

It’s absolutely possible to become a Data Engineer even if you have no prior experience in tech and no degree. In fact, a career as a Data Engineer is one of the best entry level jobs in tech. Read on to learn how to do it!


Learn The Required Skills

First things first, in order to become a Data Engineer you have to learn the required tech skills!


Python is a general-purpose coding language—which means it can be used for other types of programming and software development besides web development.

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Java is a static “object-oriented” programming language that works on multiple platforms.

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SQL stands for “Structured Query Language” and it is a programming language used to manage data in relational database management systems, creating data structures, and accessing data in web development.

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ETL processes

ETL stands for Extract, Transform, and Load. ETL processes are used to extract data from one system, transform it into a different format, and load it into another system.

Big Data

Big data is a term used to describe data sets that are too large or complex to be processed using traditional data processing methods. Big data is often used to analyze customer behavior, predict trends, and make better decisions.

Cloud Platforms

Cloud platforms refer to online services that provide scalable computing resources, storage, and services over the internet, enabling organizations to deploy, manage, and run applications without the need for on-premises infrastructure. The most commonly used cloud platforms are Amazon Web Services (AWS), Google Cloud, and Microsoft Azure.

Data warehousing

Data warehousing is the process of collecting, storing, and organizing data from a variety of sources. Data warehouses are used to store large amounts of data for analysis and reporting.

Data Structures

Data structures are data organizations that allow for efficient access and manipulation of data.


Algorithms are step-by-step procedures or sets of rules designed to solve specific problems or perform tasks in computing and other fields, helping computers process and analyze data efficiently.

Machine Learning

Machine learning is the process of developing machines, software programs, and other computer systems capable of “learning” and applying learned knowledge without specific instructions.

Data Modeling

Data modeling is the process of representing data in a way that is easy to understand and use. Data models are used to design databases, applications, and other systems that store and process data.

Distributed computing

Distributed computing is the practice of running multiple tasks on multiple computers in a network. Distributed computing is used to solve problems that are too large or complex to be solved by a single computer.

NoSQL databases

NoSQL databases are non-relational databases that provide flexible data models, suitable for handling large volumes of unstructured or semi-structured data, offering advantages in certain use cases over traditional relational databases.

Code Efficiency

Code efficiency is the ability to write code that is both fast and reliable. Code efficiency is important for a number of reasons, including performance, cost, and scalability.

Version Control

Version control is the management of changes to documents, source code, or other files, allowing multiple users to collaborate and track revisions, facilitating teamwork and preventing conflicts.

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Build A Portfolio

The best way to demonstrate that you have the necessary skills—especially when you have no prior experience—is with a portfolio of professional quality coding samples.

Check out these blog posts for more:


Apply For Tech Jobs

Once you’ve learned all the required technical skills and built a killer portfolio, it’s time to dust off that old resume and LinkedIn profile and hit the pavement, or Internet superhighway as it were, in search of your first job as a Front End Developer!

➡️ Prepare Your Resume, LinkedIn, and Portfolio

Although your most valuable asset as you job search is your portfolio, you do have to cross your t’s and dot your i’s and when it comes to the job search that means optimizing your resume and LinkedIn profile. Tech employers expect you to have all three!

Check out these blog posts for more:

➡️ Build Your Network

Your net worth is in your network, which can be hard when you’re changing careers! But don’t worry, the tech industry is incredibly welcoming to newcomers. Whether you prefer in-person meetups, Slack channels, coffee-over-zoom chats, conferences, hack-a-thons or a little bit of everything, there are tons of opportunities for you to meet fellow techies.

Check out these blog posts for more:

➡️ Find Good Jobs To Apply For

A good job can be hard to find—or is it? The good news about tech is that there are so many openings at so many diverse companies that your biggest challenge will most likely be keeping up with all the opportunities!

Check out these blog posts for more:

➡️ Practice Interviewing

Whether you’re a season pro, or brand new to the tech industry: interviewing for a new job is tough! Add to that technical interviews…and you’ve got a recipe for heartburn, practically guaranteed. Luckily there’s an antacid on the market that works every time: practice. Read on for expert guidance on how to prepare for your next tech job interview.

Check out these blog posts for more:

➡️ Prepare for Technical Tests

Ah the dreaded technical test! Technical tests can come in many different forms: whiteboard tests, pair programming tests, take-home tests, algorithmic tests…just to name a few. Luckily, getting good at technical tests is a skill, just like anything else, and it’s one you can absolutely practice ahead of time.

Check out these blog posts for more:

Keep Exploring

You Might Also Be Interested In Learning About:

Big Data

Big Data developers specialize in systems handling large amounts of data, using technologies like Hadoop or Spark.

Learn More

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  • What does a Data Engineer do?

  • How much do Data Engineers make?

  • Is a Data Engineer the same as a Quantitative Data Engineer?

  • Are Data Engineer in demand?

  • Is a Data Engineer a good job?

  • What skills does a Data Engineer need?

  • Is it too late to become a Data Engineer at 35?