What does a big data engineer actually do? How much can they earn? And what tools do they use? Find out in this post.
It’s time to talk about the elephant in the room: big data engineering. Big data engineers don’t get as much airtime as data scientists and data analysts, but they are an irreplaceable part of the data economy. While they share many of their skills with other data-related roles, a data engineer’s main focus is on making data accessible to all kinds of users. This allows organizations to use it for a variety of different tasks. Without data engineers, we would be lost.
In this article, we offer a first-step introduction to big data engineering. We’ll cover all the basics you need to know, including:
- What is data engineering, and what does a big data engineer actually do?
- What is the difference between a big data engineer, a data analyst, and a data scientist?
- How much do big data engineers earn?
- How to become a big data engineer
- What tools does a big data engineer use?
- Wrap-up and further reading
So, what does a big data engineer actually do? Let’s find out.
1. What is data engineering, and what does a big data engineer actually do?
Data engineering is crucial to any tech-driven organization. While the specifics of the role vary from job to job, its primary function is to develop, test, and maintain big data architectures, data pipelines, warehouses, and other processing systems. A data engineer’s ultimate goal is to retrieve, store, and distribute data throughout an organization.
OK, we’ll admit it… On paper, this doesn’t sound nearly as flashy as something like data analytics (which can literally predict the future.) However, all the tasks we’ve just described (which you can think of collectively as data governance) are indispensable. Without engineers bridging the gap between the chaos of big data and the order of relational databases, data scientists and data analysts wouldn’t be able to unlock data’s hidden potential.
While data engineers may not be known for making impressive discoveries, without them working their magic, transforming data into a format everyone can use, nor would anybody else. Seen this way, data engineering becomes far more appealing. Data engineers are like the wizards of the data world—without their skills, none shall pass!
What does a big data engineer do?
OK, so we’ve covered the basics. But what does a data engineer’s day-to-day job look like? What are their tasks and responsibilities? To offer an idea, here are some taken from real job ads.
Big data engineering responsibilities
- Design, create, and manage scalable ETL (extract, transform, load) systems and pipelines for various data sources
- Manage, improve, and maintain existing data warehouse and data lake solutions
- Optimize and improve existing data quality and data governance processes to improve performance and stability
- Build bespoke tools and algorithms for the data science and data analytics teams (and other data-driven teams across the business)
- Work closely with business intelligence teams and software developers to define strategic objectives as data models
- Work closely with the wider IT team to manage the business’s wider infrastructure
- Explore the next generation of data-related tech to expand the organization’s capacity and maintain a competitive edge
Ideal skills for big data engineers
- Critical thinking, excellent communication, team working, and problem-solving
- A degree in computer science (or similar role-related field)
- Several years of experience in software development or data management
- Strong technical background with knowledge of numerous programming languages, and a general love of writing code
- Hands-on experience using Python and SQL, and big data technologies like the Apache Stack
- Experience using relational database management systems, e.g. PostgreSQL, MySQL
- Understanding of batch and real-time data integration, data replication, data streaming, virtualization, and so on
While a big data engineer will likely have a natural flair for things like problem-solving, the more practical skills (such as different technologies) can be learned. So don’t panic if you looked at this list and thought “I don’t know any of this!” Instead, see it as a challenge—start by picking one item on the list and then familiarize yourself with it.
2. What is the difference between a big data engineer, a data analyst, and a data scientist?
Often, the terms data scientist, data analyst, and data engineer are used interchangeably. However, they are different roles, so let’s define them more accurately. While they share many data-related skills, each role nevertheless has a distinct function. Understanding this is vital for distinguishing between them. Let’s look at these functions now.
What is a big data engineer’s function?
A big data engineer’s primary function is to manage and maintain big data infrastructures.
This involves collecting, storing, and distributing data across an organization. Crucially, there’s a strong development aspect to a data engineer’s role. As such, you’ll find that data engineers often start their careers as software developers. Refer back to section one for more specific details on what the job involves.
What is a data analyst’s function?
A data analyst’s primary function is to draw insights from data to inform decision-making.
While the data analyst’s role incorporates a wide range of data-related tasks (from collecting and cleaning to structuring data) it primarily involves spotting and interpreting trends to solve clearly defined business problems. Data analysts may be part of the wider business intelligence team or they may be embedded in a particular department (with specific area expertise).
A data analyst might use customer usage data to identify which features of a product need improvement. Or they might use data to devise more efficient supply chain strategies.
Data analysts rely heavily on the infrastructure that big data engineers create and maintain. While they can also manipulate these structures to a degree (for instance by calling up data in relational databases using SQL) they are unlikely to have the same depth of knowledge of these technologies as a big data engineer.
What is a data scientist’s function?
While a data analyst extracts insights from data, a data scientist’s primary function is to construct the methods for extracting these insights from big data.
Their role is similar to that of a data analyst but much more high-level. A data scientist will devise entirely new models or analytical techniques for others to use. And while analysts know about a particular area of the business, e.g. sales or product design, a data scientist takes a helicopter view. They have oversight of the wider business strategy and focus on opportunities that benefit the whole business. Often, data scientists have a background in senior management or leadership (as well as data science).
Data scientists are usually talented data engineers in their own right. It is simply that data engineering is a complex and time-consuming task. Having a dedicated engineer saves data scientists a lot of time, although the two roles still work closely together. The blurred lines between data scientists and data engineers are why you’ll often hear the terms used synonymously.
Despite the shared skills, the main thing to take away from this section is the different functions of each role. While there’s always some crossover, to recap:
- A data engineer manages and maintains big data infrastructures
- A data analyst draws insights from data to inform decision-making
- A data scientist primarily develops the methods for extracting these insights
3. How much do big data engineers earn?
Next up, something more straightforward… money! What does a big data engineer’s salary look like? Naturally, the answer to this question varies depending on work experience, job title, geographic location, as well as the company.
But taking an average of estimates from a range of job and salary comparison sites (Glassdoor, Payscale, Salary Expert, and salary.com) we’ve determined that big data engineers in the United States earn an average of $108K. The actual amount can be even higher once you consider things like bonuses or geographic weighting (for instance, data engineers in Europe can earn even more).
How does a big data engineer’s salary compare to that of data analysts and data scientists? According to salary.com, data analysts in the US earn an average of $77K, while data scientists earn an average of $132K. While these are just estimates, none of these figures are bad at all… and all of them are above the US national average.
For more in-depth breakdowns, you can learn more about a data engineer’s salary here.
4. How to become a big data engineer
You know what a big data engineer does and you know how their job differs from data analytics and data science. You even know how much you could earn. But how do you become one? In this section, we briefly highlight some things you can do to land yourself a job in the field:
- Get a relevant degree: Data engineers need to be properly qualified. You’ll likely need a degree (Master’s or higher) in a field like computer science, software engineering, physics, or applied math.
- Consider a certified course: If you already have a degree or don’t want to go down that route, another option is to take a certified online course to ‘top up’ your relevant skills. This could be in an area like data analytics, machine learning, or software development. You’ll find a comparison of some of the best data analytics certification programs here.
- Get work experience: To land a job as a big data engineer, you’ll usually need some prior work experience. Perhaps you’ve been a software developer or data analyst, or maybe you worked as an intern? You could also create a portfolio of your work.
- Familiarize yourself with databases: Databases are the basic building blocks of all big data architectures. Be sure to get the theory down, and train yourself using tools like SQL and database management systems (like those described in section five).
- Develop some broader skills: There are many tools you can use as a data engineer. You don’t need to be an expert in all of them, but it helps to understand what kinds are out there and how they interact with each other.
- Remain open to any job opportunities: More often than not, data engineers start their careers in different roles, either as software developers, data professionals, or through an academic route. Early on in your career, be open to taking any job that is related to data engineering, even if it’s not the job you always dreamed of!
You can learn more about what it takes to become a big data engineer here.
5. What tools does a big data engineer use?
If you’ve decided this is the route for you… where next? First up, consider playing around with a few of the tools that big data engineers commonly use. This is a great way of getting a feel for whether data engineering might be suitable for you. The basics include things like MS Excel and the fundamentals of system design. However, big data engineers also use a range of different technologies. A few of these include:
- Python (and other programming languages)
- ETL tools
- SQL and NoSQL
- PostgreSQL (or another database management system)
- Apache Spark (and to a lesser extent, Hadoop)
- Amazon S3
Let’s look at each of these in more detail now.
Python (and other programming languages)
The Python programming language is an increasingly staple requirement for anyone working in data. Versatile and easy to learn, it emerged in recent years as a popular language that transcends the boundaries of expertise. This is precisely why it’s so useful— by using the same language, those working in different areas of data can streamline the integration of their work.
Of course, you can just as readily use other programming languages if you know them already. These might include Javaor Scala. Python is simply the most popular. You can learn more about Python here.
ETL Tools
Extract, Transform, Load (ETL) tools are a group of technologies used for transferring data from one system or infrastructure to another. Essentially, they allow users to pull data from various sources (extract), consolidate these data into new formats (transform), and then transfer them into a new database or system (load).
ETL can be carried out with programming languages like Python or with proprietary software designed specifically with the task in mind, e.g. Xplenty or Talend.
SQL and NoSQL
SQL (Structured Query Language) is a domain-specific language used for communicating with relational databases. Meanwhile, NoSQL refers to frameworks that store data in a non-relational format.
As tools, this simply means that big data engineers can use SQL to communicate with data stored in a predefined tabular format (relational databases) but that they can also work with unstructured big data stored in what we might call a ‘shopping list’ format. This is often a document or file (non-relational database).
PostgreSQL (or another database management system)
PostgreSQL is a free, open-source relational database management system. Built to be SQL compliant, it’s a common type of tool that many data engineers use. While PostgreSQL has its origins back in the mid-1990s, it has proven popular in the digital age and is commonly used as a data warehouse for many popular web and mobile apps (which, as we know, are the source of many forms of data).
Alternatives to PostgreSQL include other open-source solutions like MySQL. Enterprise systems are also available, like Microsoft SQL and Oracle Database.
Apache Spark (and to a lesser extent, Hadoop)
When your job involves working with vast amounts of big data, single databases on one computer often won’t cut it. That’s when you need to invest time in distributed computing systems like Apache Spark or Hadoop.
These tools spread large datasets across clusters of computers. Older Hadoop is quite expensive to implement and complex to use, yet remains prevalent because of its legacy usage. More recently, however, it has been edged out by Apache Spark, a much faster system that is better suited to emerging techniques like machine learning.
Amazon S3
There are loads of commercial tools out there for big data engineers—if we were to list them all, we’d be here a while! However, one example is Amazon S3. The Amazon S3 web service allows users to store and retrieve any volume of data from anywhere on the web. Essentially, it gives developers access to the same infrastructure that Amazon itself uses to manage its global network of websites.
This highlights how technologies that are currently evolving are being adapted to modern requirements. Many big data engineers even specialize in Amazon S3 and other Amazon web services (AWS).
While this list provides just a small taste of the tools you might come across, we would encourage you to go out there and explore some of your own. Ask yourself: what do you want to specialize in? And from there…What tools might help you?
6. Wrap-up and further reading
In this post, we’ve explored what a big data engineer actually does. We’ve taken you on a tour through the field, looking at how big data engineering differs from data analytics and data science. We’ve learned that a data engineer can earn a pretty comfortable living, and offered some tips on how to jump-start your new career!
While data engineering may not be as high-profile as other data-related roles, it’s an in-demand job that can be highly rewarding.
To learn more about what a career in data involves, why not try this free, five-day data analytics short course?
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