{"id":4321,"date":"2021-05-19T12:54:04","date_gmt":"2021-05-19T10:54:04","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/?p=4321"},"modified":"2023-05-11T21:24:10","modified_gmt":"2023-05-11T19:24:10","slug":"become-a-big-data-engineer","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/become-a-big-data-engineer\/","title":{"rendered":"How To Become a Big Data Engineer"},"content":{"rendered":"

So you want to become a big data engineer? Good choice! But how to go about it? Find out in this post.<\/strong><\/p>\n

Back in 2012, Harvard Business Review announced that data scientist was officially the “sexiest job of the 21st century<\/a>.” Today, this is changing.<\/p>\n

In the weird, wonderful, and fast-evolving world of data, the specialized role of big data engineer is fast becoming indispensable. Data engineers don\u2019t get as much hype as data analysts and data scientists, but without them, data would remain an inscrutable mass of meaningless information.<\/p>\n

In fact, according to a 2019 report commissioned by the Royal Society (the UK\u2019s national academy of sciences)\u00a0data engineering is becoming the UK\u2019s most in-demand data-related role<\/a>. International trends are following suit. So, a career in data is an excellent move\u2014and big data engineering is just one exciting path you might choose to follow.<\/p>\n

In this post, we\u2019ll explore how to become a big data engineer. We\u2019ll cover:<\/p>\n

    \n
  1. What does a big data engineer do?<\/a><\/li>\n
  2. Big data engineer vs. data scientist: What’s the difference?<\/a><\/li>\n
  3. Why become a big data engineer?<\/a><\/li>\n
  4. Big data engineer salary: How much do big data engineers earn?<\/a><\/li>\n
  5. What is the typical background of a big data engineer?<\/a><\/li>\n
  6. Big data engineer roadmap: How to become a big data engineer<\/a><\/li>\n
  7. Wrap-up and further reading<\/a><\/li>\n<\/ol>\n

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

    1. What does a big data engineer do?<\/h2>\n

    OK, first things first. What does a data engineer actually do?<\/p>\n

    In short, a big data engineer develops, manages, and maintains an organization\u2019s data infrastructure. Their job involves collecting and transforming data and storing it in databases in a format that\u2019s easier for others to use. They generally work with senior leadership, data analysts, and data scientists. This helps ensure their models and systems are closely aligned with the needs of individual teams, as well as with the wider business strategy. With all this responsibility, data engineers tend to work in rather senior roles.<\/p>\n

    To help put the role of the data engineer in context, you might also want to check out the role of the data analyst<\/a>, as well as the difference between a data engineer and a data scientist<\/a>.<\/p>\n

    What tasks does a big data engineer carry out?<\/h3>\n

    A big data engineer\u2019s tasks all revolve around making sense of vast, unstructured datasets. You can learn more about the difference between structured and unstructured data (and why it matters) in this guide<\/a>. Ultimately, data engineers must bring datasets into some sense of order. A competent data engineer, therefore, is just another type of data scientist.<\/p>\n

    However, rather than focusing on broader strategic questions, their focus is on creating structure and carefully designing bespoke systems. This can involve developing data pipelines (for funneling data into data lakes or warehouses). It can mean developing specialist data analytics algorithms for other areas of the business. And it most certainly involves becoming an expert at using a wide range of different big data technologies and programming languages.<\/p>\n

    This should give you a broad flavor but you can learn more about what a big data engineer does here<\/a>.<\/p>\n

    2. Big data engineer vs. data scientist: What’s the difference?<\/h2>\n

    A big data engineer is primarily responsible for the building (and maintaining) of the systems and processes that collect and extract big data.<\/p>\n

    A data scientist is responsible for organizing the big data within the architecture built by the big data engineer, and performs in-dept analyses of this data to help form insights and make business decisions.<\/p>\n

    Big data engineers and data scientists will often work closely together\u2014after all, a data scientist can not obtain insights from data without the data itself!<\/p>\n

    Related reading:<\/strong> What’s the difference between a data scientist and a data engineer?<\/a><\/p>\n

    3. Why become a big data engineer?<\/h2>\n

    Everyone\u2019s always saying how great data analytics or data science can be, but is big data engineering a rewarding career path, too? The short answer is a resounding yes. But with a caveat\u2014it\u2019s challenging! Overcoming these challenges is part of the reward, however, as we\u2019ll see. Here are a few reasons why big data engineering can be a great career choice.<\/p>\n

    An opportunity (and motivation!) to perfect your programming skills<\/h3>\n

    One of the most important skills for a big data engineer is the ability to code. Whether you\u2019re using Python or Scala, JavaScript or C#, data engineering offers you an unrivaled opportunity to take your programming skills to the next level. You can even learn a new language or two.<\/p>\n

    Building data pipelines; managing data system security; creating scalable databases, or simply auditing data\u2014all these vital tasks involve excellent programming skills. Since many data engineers come from a software engineering background, this is one challenge that should certainly appeal!<\/p>\n

    Flexibility to choose your path<\/h3>\n

    In all honesty, becoming a data engineer can be hard. But once you\u2019ve nailed the key skills and landed your first job, you\u2019ll find plenty of freedom to develop your dream role. You\u2019ll get to choose what you\u2019re working on and when, and will rarely be told what tools to use. This keeps things fresh while offering myriad opportunities to develop new software skills.<\/p>\n

    Because data engineers require a grounding in data management and software development, there\u2019s also a much wider scope for the sort of output you produce. Maybe you want to work as part of a team of engineers for a big data-driven business? Or perhaps you\u2019d rather be the lone ranger expert for a tech start-up? The choice is yours. The options are broad and no two roles will ever be the same.<\/p>\n

    You\u2019ll develop a broader business and analytical focus<\/h3>\n

    Most often, software developers create products for customers or internal users. This can quickly descend into responding to bug reports, resolving software issues, and so on. As any developer will tell you, this can feel a bit like fire fighting! However, once you bring data into the equation, everything changes.<\/p>\n

    With data skills under your belt, you can develop new products and analyze their performance in real-time. And using data to measure impact and drive effective change is exactly what makes data engineers so valuable. As you shift away from hardware support towards strategic input, you\u2019ll become increasingly involved in making decisions that impact the business. This can feel like a lot of responsibility but it\u2019s also highly rewarding.<\/p>\n

    Ideal for remote working<\/h3>\n

    While there\u2019s been a boom in remote working since the coronavirus pandemic, not all jobs are ideally suited to working from home. Luckily, that\u2019s not the case for big data engineering. So long as you have the right hardware, security, and a stable internet connection, most data engineering tasks can be carried out remotely.<\/p>\n

    As an in-demand role, you\u2019ll also find organizations are generally more willing to accommodate your needs\u2014leverage that! And, of course, if you want to work as a contractor, working remotely is practically a requirement. Bonus!<\/p>\n

    Contribute to open-source projects<\/h3>\n

    While you may find yourself working for a private organization, that doesn\u2019t mean your sphere of influence stops there. Many of the tools that software engineers use are open source (meaning that their code is freely available to develop and share).<\/p>\n

    According to the\u00a02023 Stack Overflow Developer Survey<\/a>, approximately 25% of software engineers<\/strong> contribute to open source projects outside of work. Throwing data engineering skills into the mix adds even greater value to open source projects. With your specialist skills, you can make a real-world difference in the community that goes far beyond the organization you work for.<\/p>\n

    On top of these reasons to get into data engineering, there\u2019s one more we haven\u2019t yet covered, which you may be wondering about: salary.<\/p>\n

    \"A<\/p>\n

    4. Big data engineer salary: How much do big data engineers earn?<\/h2>\n

    Okay, let\u2019s get down to the details: what does a big data engineer\u2019s salary look like? With a background in areas like software development, data management, and academia, most data engineers already have a solid career foundation to build on. So it\u2019s a safe bet to say they can earn a pretty decent salary as a data engineer, too.<\/p>\n

    To get a gauge, we took an average of estimates from the job and salary comparison sites\u00a0Glassdoor<\/a>,\u00a0Payscale<\/a>,\u00a0Salary Expert<\/a>, and\u00a0salary.com<\/a>. Crunching the numbers tells us that the average big data engineer in the United States can earn about $110,651 per year.<\/strong> This figure isn\u2019t set in stone\u2014the real amount depends on factors like work experience, specialist skills, location, and the company you\u2019re working for. But it gives you a pretty good idea.<\/p>\n

    To dig into more detail on this, we’ve created a comprehensive big data engineer salary guide<\/a>.<\/p>\n

    5. What is the typical background of a big data engineer?<\/h2>\n

    Until recently, you wouldn\u2019t find many people who started their career thinking: \u201cYou know what? I\u2019m going to become a data engineer!\u201d<\/p>\n

    Roles for professionals in the field of data have typically emerged as the need has arisen. For instance, many of those entering data engineering today started as software developers who required some data and engineering skills to do their job better. Another example is big data enthusiasts who get a buzz out of bringing order to all that chaos. By simply playing around, many realized that they had a skill and that there was a demand for these skills.<\/p>\n

    While there\u2019s no single route into the field, most engineers indeed start their careers in a select number of other fields. These fields nurture the specific skillsets which lend themselves well to big data engineering. The most common backgrounds for data engineers include:<\/p>\n