{"id":14406,"date":"2023-03-14T07:26:39","date_gmt":"2023-03-14T14:26:39","guid":{"rendered":"https:\/\/www.springboard.com\/?p=14406"},"modified":"2025-01-27T04:04:14","modified_gmt":"2025-01-27T12:04:14","slug":"how-to-become-data-engineer","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/how-to-become-data-engineer\/","title":{"rendered":"How To Become a Data Engineer [2025 Career Guide]"},"content":{"rendered":"\n

Though data scientists may garner more headlines than other data professionals, data science isn\u2019t necessarily the most exciting career in the data space. Today, the growing demand for data engineers is actually outpacing the growing demand for data scientists\u2014between 2021 and 2022, there was a 100% increase in available data engineering roles, according to DataCamp. <\/p>\n\n\n\n

If you\u2019re looking to cash in on this trend, then you\u2019re in the right place. Below, we\u2019ll tell you everything you need to know about how to become a data engineer, and what to expect once you\u2019re hired. <\/p>\n\n\n\n

Ready? Then let\u2019s get started. <\/p>\n\n\n\n

What Does a Data Engineer Do?<\/h2>\n\n\n\n

Data engineers are the town planners of the data world. They are the professionals who come up with the architecture of data systems and build pipelines to channel data through different parts of the system. They may also build relational databases and data warehouses. <\/p>\n\n\n\n

Is It Hard To Become a Data Engineer?<\/h2>\n\n\n\n

Data engineering is a highly technical role. You need to have a strong grasp of computing, data handling, and, in certain cases, areas like cloud computing and machine learning. The key is to build a strong theoretical foundation and gain practical experience through personal projects, internships, and networking. <\/p>\n\n\n\n

How To Become a Data Engineer: A 9-Step Guide<\/h2>\n\n\n\n
\"how<\/figure>\n\n\n\n

Below, we\u2019ll cover the essential skills you require for a career in the data engineering field and what you can do to impress potential employers.<\/p>\n\n\n

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\n\n<\/div>\n\n
    \n
  1. \n

    Complete a Course<\/p>\n

    <\/div>\n<\/li>\n
  2. \n

    Get Familiar With the Industry and Lay a Solid Foundation<\/p>\n

    <\/div>\n<\/li>\n
  3. \n

    Meet the Educational Requirements (And Go Beyond)<\/p>\n

    <\/div>\n<\/li>\n
  4. \n

    Learn a Programming Language or Two<\/p>\n

    <\/div>\n<\/li>\n
  5. \n

    Nurture the Important Skills<\/p>\n

    <\/div>\n<\/li>\n
  6. \n

    Gain Practical Experience by Working on Projects and Building Your Portfolio<\/p>\n

    <\/div>\n<\/li>\n
  7. \n

    Your Network Is Your Net Worth (And Can Help You Land a Job)<\/p>\n

    <\/div>\n<\/li>\n
  8. \n

    Try To Find a Mentor<\/p>\n

    <\/div>\n<\/li>\n
  9. \n

    Pursue an Internship<\/p>\n

    <\/div>\n<\/li>\n
  10. \n

    Polish Your Resume Ready and Start Applying<\/p>\n

    <\/div>\n<\/li>\n<\/ol>\n<\/div>\n\n\n\n\n\n\n

    Complete a Course<\/h3>\n\n\n\n

    It’s really difficult – but not impossible – to learn the skills you need to become a data engineer on your own. However, a simple six-month bootcamp<\/a> is the most affordable, and quickest, way to launch your career without pursuing a formal education. You’ll get all of the help (and the skillset) you need in the most convenient way possible. <\/p>\n\n\n\n

    Get Familiar With the Industry and Lay a Solid Foundation<\/h3>\n\n\n\n

    Data engineers have to have a love for data and need to understand how it\u2019s employed in the service of real-world problems<\/a>. But data engineers also need to have a solid understanding of their industry, whether that be finance, healthcare, or marketing. Because while it\u2019s important to know how to work with complex datasets and engineer relational databases, none of that will be very fruitful if you don\u2019t understand the business context to which your work contributes. <\/p>\n\n\n\n

    Meet the Educational Requirements (And Go Beyond)<\/h3>\n\n\n\n

    A college degree in business analytics, computer science, or mathematics is a great starting point for a career as a data engineer. That said, a college degree is also a costly and time-consuming way to go about picking up these skills. <\/p>\n\n\n\n

    An alternative is to do a data engineering bootcamp<\/a>. The advantage of choosing the bootcamp route is that it places a strong emphasis on picking up data engineering skills and you\u2019ll pick up key skills without having to complete classes on any extraneous topics. <\/p>\n\n\n\n

    Whichever of those paths you choose, you can\u2019t stop learning there. Data engineering is a field with a wide range of analytical approaches and tools used in the trade. So completing data engineering online courses<\/a> and professional certificates can help you level up.<\/p>\n\n\n\n

    Learn a Programming Language or Two<\/h3>\n\n\n\n
    \"how
    Source: Treasure Data<\/a><\/figcaption><\/figure>\n\n\n\n

    Programming is a skill that you might not always use in your job as a data engineer. But it is one that prospective employers like to see on your resume. Here are a few that you should consider learning: <\/p>\n\n\n\n

    Python<\/h4>\n\n\n\n

    Python is one of the most commonly used programming languages in data engineering and software engineering in general. If you\u2019re just starting out, it\u2019s a good language to use to understand basic concepts in programming. <\/p>\n\n\n\n

    SQL<\/h4>\n\n\n\n

    SQL (Server Query Language) is the most popular language that\u2019s used to work with data in relational databases. As a data engineer, it\u2019s essential that you have a knowledge of databases and hands-on experience using SQL to work with them. <\/p>\n\n\n\n

    Java<\/h4>\n\n\n\n

    Java is a programming language that\u2019s used to build robust cross-platform applications. Software engineers use Java extensively in their work and it has slowly come to be used in data engineering as well. <\/p>\n\n\n\n

    R<\/h4>\n\n\n\n

    R is a programming language used for statistical analyses. It\u2019s a language that you must be familiar with if you build analytical tools or your work involves a heavy amount of number crunching. <\/p>\n\n\n\n

    Others<\/h4>\n\n\n\n

    Scala is another popular programming language for working with data<\/a>. It is extremely good for projects that are data-intensive. Another advantage of Scala is that it runs on the Java virtual machine, which means that you can easily use it to build web apps and mobile services. <\/p>\n\n\n\n

    Nurture the Important Skills<\/h3>\n\n\n\n

    Programming is only a small part of the broad skill set you require as a data engineer. There are various other in-demand skills in the field. Let\u2019s take a look at what they are.<\/p>\n\n\n\n

    \"how
    Source: stratascratch<\/a><\/figcaption><\/figure>\n\n\n\n

    Technical Skills<\/h4>\n\n\n\n

    The most obvious technical skill that data engineers need to have is working with data. You should be adept at data mining<\/a>, cleaning, and transformation. As you make progress in your career path, you should pick up tools and APIs that help you carry out those processes in an efficient manner. <\/p>\n\n\n\n

    Data engineers are increasingly required to know the fundamentals of cloud computing. This is because their work often requires working with a cloud-based data warehouse or using cloud data services. So it helps if you have a working knowledge of cloud technology too. <\/p>\n\n\n\n

    Soft Skills<\/h4>\n\n\n\n

    The job of a data engineer isn\u2019t a straightforward one. You\u2019ll often be thrust into situations where your problem-solving abilities<\/a> will be tested. You can improve on this skill by working on a wide variety of projects and using new tools and approaches whenever possible. <\/p>\n\n\n\n

    Data engineering is a collaborative process, and you\u2019ll often have to work with business analysts and project managers to understand the business consequences of your work. This is where communication becomes key. A good data engineer should be able to talk about their work with various stakeholders, even nontechnical ones. <\/p>\n\n\n\n

    Gain Practical Experience by Working on Projects and Building Your Portfolio<\/h3>\n\n\n\n

    Here are a few ways you can gain some real-world experience and have projects that you can put in your portfolio. <\/p>\n\n\n\n

    Volunteer Work<\/h4>\n\n\n\n

    Volunteering is perhaps the most low-pressure way to work on real-world projects. It\u2019s not always easy to find volunteer work as a data engineer, though, so you will have to reach out to organizations around you to see if they\u2019re hiring in the field. If you do find such an opportunity, it\u2019s a great way to ease into production work as a data engineer. <\/p>\n\n\n\n

    Freelance Work<\/h4>\n\n\n\n

    Freelancing as a data engineer, especially a new one, isn\u2019t always easy. You\u2019ll need to choose freelance projects that are limited in scope or where you work under the guidance of a senior engineer. <\/p>\n\n\n\n

    Sample Projects<\/h4>\n\n\n\n

    Building sample data engineering projects<\/a> is the best way to gain some practical experience. You can choose to work on something that you\u2019re interested in personally. You can also take things a step forward by putting your code on Github<\/a> so that others can look at your work and provide feedback.<\/p>\n\n\n

    Get To Know Other\tData Science Students<\/h4>
    \"Jonas<\/a>

    Jonas Cuadrado<\/p>

    Senior Data Scientist at Feedzai<\/p><\/div>

    <\/div>

    Read Story<\/a><\/p><\/div><\/div>

    \"Pizon<\/a>

    Pizon Shetu<\/p>

    Data Scientist at Whiterock AI<\/p><\/div>

    Read Story<\/a><\/p><\/div><\/div>

    \"Bret<\/a>

    Bret Marshall<\/p>

    Software Engineer at Growers Edge<\/p><\/div>

    Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

    Your Network Is Your Net Worth (And Can Help You Land a Job)<\/h3>\n\n\n\n

    Networking is huge if you want to land a job as a data engineer. Here are a few avenues to build your network as a data engineer: <\/p>\n\n\n\n

    LinkedIn<\/h4>\n\n\n\n

    LinkedIn is great if you want to network, as you don\u2019t have to meet people face-to-face immediately and you can build relationships slowly. But you do need to put effort into nurturing relationships on LinkedIn by engaging with your connections\u2019 posts regularly and posting your own content. <\/p>\n\n\n\n

    Online Communities<\/h4>\n\n\n\n

    Online communities don\u2019t necessarily give you the opportunity to meet recruiters or senior engineers. But you can meet others who are passionate about data engineering, which can lead to collaborations and referrals. <\/p>\n\n\n\n

    Here are a few online communities where you can meet other data engineers and other professionals in the field:<\/p>\n\n\n\n