What does a Big Data Engineer do? [Role, Salary and Skills] (2023)

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 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 primary focus is on making data accessible to all types of users. This allows organizations to use it for a variety of different tasks. Without data engineers, we would be stuck.

In this article, we offer a first introduction to big data engineering. We've covered all the basics you need to know, including:

1. What is data engineering and what does a big data engineer actually do?
2. What is the difference between a Big Data Engineer, a Data Analyst and a Data Scientist?
3. How much do Big Data engineers earn?
4. How to become a big data engineer
5. What tools does a big data engineer use?
6. Conclusion and further reading

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 essential for any technology-focused organization. While the specifics of the role vary from job to job, their primary role is to design, test, and maintain big data architectures, data pipelines, warehouses, and other processing systems. The ultimate goal of a data engineer is the retrieval, storage and distribution of data throughout the enterprise.

Okay, let's admit it... On paper, this doesn't sound as flashy as something like data analytics (which can literally predict the future). asdata office) essential. Without engineers to bridge the gap between the chaos ofbig dataand the order of relational databases, data scientists and analysts have failed to unlock the hidden potential of data.

While data engineers may not be known for making amazing discoveries without working their magic and transforming the data into a format anyone or anyone else can use, this way data engineering becomes infinitely more attractive. Data engineers are like the wizards of the data world without their skills.nothing will happen!

What does a Big Data Engineer do?

Okay, we've covered the basics. But what does a data engineer's day job look like? What are your duties and responsibilities? To give you an idea, here are some real job advertisements.

Big data engineering manager.

• Design, build and manage scalable ETL (Extract, Transform, Load) systems and pipelines for multiple data sources
• Manage, enhance and maintain existing data warehouse and data lake solutions
• Optimize and improve what already existsdata qualityand data governance processes to improve performance and stability
• Build custom tools and algorithms for data science and analytics teams (and other data-driven teams across the organization)
• Work closely with business intelligence teams and software developers to define strategic goals like data models.
• Work closely with the broader IT team to manage the overall business infrastructure.
• Discover the next generation of data-centric technologies to extend business capability and maintain a competitive edge

Ideal Skills for Big Data Engineers

• Critical thinking, excellent communication, teamwork and problem solving.
• Completed degree in computer science (or comparable professional subject)
• Several years of experience in software development or data management.
• Strong technical background with knowledge of multiple programming languages ​​and a general love of writing code
• Hands-on experience with Python and SQL, as well as big data technologies like Apache Stack
• Experience dealing with relational database management systems, e.g. PostgreSQL, MySQL
• Understanding of batch and real-time data integration, data replication, data streaming, virtualization, etc.

While a big data engineer is likely to have a natural gift for things like problem solving, more practical skills (e.g. different technologies) can be learned. So don't panic if you look at this list and think, "I don't know anything about this!" Instead, take it as a challenge: pick one from the list first, then explore.

2. What is the difference between a Big Data Engineer, a Data Analyst and a Data Scientist?

The terms data scientist, data analyst and data engineer are often used interchangeably. However, these are different functions, so let's define them more precisely. Although they share many data-related skills,However, each role has its own function.. Understanding this is important for distinguishing between them. Let's see these features now.

What is the role of a big data engineer?

The main job of a big data engineer is to manage and maintain big data infrastructures.

This includes collecting, storing and distributing data within an organization. Fundamentally, the role of a data engineer has a strong developmental aspect. Therefore, you will find that data engineers often start their careers as software developers. Review section one for more details on what the job entails.

What is the role of a data analyst?

The main role of a data analyst is to extract information from data to support decision making..

While the data analyst role covers a wide range of data-related tasks (from collecting and cleaning to structuring data), it is primarily concerned with identifying and interpreting trends to solve well-defined business problems. Data analysts can be part of a larger business intelligence team or integrated into a specific department (with expertise in a specific domain).

A data analyst can use customer usage data to determine which features of a product need improvement. Or they use data to develop more efficient supply chain strategies.

Data analysts rely heavily on the infrastructure that big data engineers build and maintain. While they can also manipulate these structures to some extent (for example, retrieving data from relational databases using SQL), they likely don't have the same knowledge of these technologies as a big data engineer.

What is the role of a data scientist?

While a data analyst obtains information from the data,A data scientist's main job is to build the methods to extract these insights from big data..

Your role is similar to that of a data analyst, but at a much higher level. A data scientist will develop entirely new models or analysis techniques for others to use. And while analysts know a specific business area, e.g. Sales or product design, a data scientist observes from a helicopter. They have an overview of the broader business strategy and focus on opportunities that benefit the entire organization. Often, data scientists have executive management or leadership (as well as data science) experience.

Data scientists are often talented data engineers. It turns out 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 together. The blurring of lines between data scientists and data engineers is why the terms are often used interchangeably.

Despite the common abilities, the key points of this section are the different roles of each role. While there are always some crossovers, to recap:

• a data engineermanages and maintains large data infrastructures
• a data analystextract information from datato inform decision-making
• First, a data scientistdevelop the methods to obtain this knowledge

3. How much do Big Data engineers earn?

Then something simpler... money! What is the salary of a big data engineer? Of course, the answer to this question varies by professional experience, job title, geographic location, and company.

But if you take an average of estimates from various job and salary comparison sites (glass door,Löhnskal,salary expert, YWage. with) We found itBig data engineers in the US earn an average of $108,000. The actual value can be even higher when things like bonuses or geographic weighting are taken into account (eg data engineers in Europe can earn even more).

How does a big data engineer's salary compare to data analysts and data scientists? According to Salary.com, data analysts earn in the United Statesaverage $77,000, while data scientists earnan average of $132,000. While these are just estimates, none of these numbers are bad... and all are above the US national average.

For more detailed breakdowns, you canLearn more about a data engineer salary here.

4. How to become a big data engineer

You know what a big data engineer does, and you know how their work differs from data analysis 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 secure a job in this field:

• Get a relevant title: Data engineers must be suitably qualified. You will likely need a degree (master's or higher) in a field such as computer science, software engineering, physics or applied mathematics.
• Consider a certified course:If you already have a degree or don't want to go down this route, another option is to take a certified online course to refresh your relevant skills. This could be in an area like data analysis, machine learning or software development. you will findHere's a comparison of some of the best data analytics certification programs..
• Gain work experience:Landing a job as a big data engineer usually requires some work experience. Perhaps you were a software developer or data analyst or worked as an intern? You can also create a portfolio of your work.
• Get familiar with the databases:Databases are the building blocks of all big data architectures. Make sure you have a good understanding of the theory and train yourself in using tools like SQL and database management systems (such as those described in Section 5).
• Develop some broader skills:There are many tools you can use as a data engineer. You don't have to be an expert on all of them, but it helps to understand what species exist and how they interact with each other.
• Stay open to all job offers:More often than not, data engineers start their careers in a variety of roles, whether as software developers, data specialists, or through an academic path. Be open to any data engineering job early in your career, even if it's not the job you've always dreamed of!

Learn more about what it takes to become a big data engineer here.

If you've decided this is the path for you... where do you go next? First, consider playing around with some of the tools big data engineers often use. This will give you a good idea of ​​whether data engineering might be right for you. The basics include things like MS Excel and the basics of system design. However, big data engineers also use a number of different technologies. Some of them are:

• Python (and other programming languages)
• ETL Tools
• SQL e NoSQL
• PostgreSQL (or other database management system)
• Apache Spark (and Hadoop to a lesser extent)
• Amazonas S3

Let's take a closer look at these now.

Python (and other programming languages)

The Python programming language is an increasingly essential requirement for anyone working with data. Versatile and easy to learn, it has become a popular language in recent years, pushing the boundaries of experience. That's exactly why it's so useful: By using the same language, those working in different data domains can speed up the integration of their work.

Of course you can also use other programming languages ​​if you already know them. This may includeJavaoScale. Python is simply the most popular. He canLearn more about Python here.

ETL Tools

Extract, Transform, and Load (ETL) tools are a group of technologies used to transfer data from one system or infrastructure to another. Essentially, they allow users to extract (pull) data from different sources, consolidate (transform) that data into new formats, and then transfer (load) it into a new database or system.

ETL can be performed using programming languages ​​such as Python or using proprietary software designed specifically for the task, for example,Quiteotalend.

SQL e NoSQL

SQL (Structured Query Language) is a domain-specific language used to communicate with relational databases. Meanwhile, NoSQL refers to structures 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 they can also work with unstructured big data stored in the so-called "list format". shopping". . This is usually a document or file (non-relational database).

PostgreSQL (or other database management system)

postgresqlis a free and open source relational database management system. It supports SQL and a common type of tool used by many data engineers. Although PostgreSQL has its origins in the mid-1990s, it has proved popular in the digital age and is widely used as a data store for many popular web and mobile applications (which, as we know, are the source of many forms of data ). . ).

Alternatives to PostgreSQL include other open source solutions such asmysql. Corporate systems are also available such as Microsoft SQL andoracle database.

Apache Spark (and Hadoop to a lesser extent)

When your work involves large amounts of big data, individual databases on a computer are often not enough. So you have to invest time in distributed computing systems like Apache Spark or Hadoop.

These tools distribute large data sets across clusters of computers. Legacy Hadoop is quite expensive to implement and complex to use, but it is still widespread due to its legacy usage. However, more recently it has been superseded by Apache Spark, a much faster system better suited to new techniques such as machine learning.

Amazonas S3

There are numerous business tools for big data engineers. If we were to list them all, we'd be here for a while! However, an example is Amazon S3. The Amazon S3 web service allows users to store and retrieve any amount of data from anywhere on the Internet. Essentially, developers get access to the same infrastructure that Amazon uses to run its global network of websites.

This illustrates how evolving technologies are being adapted to modern requirements. Many big data engineers even specialize in Amazon S3 and other Amazon Web Services (AWS).

While this list is just a small sampling of the tools you might find, we encourage you to venture out and explore some of your own. Ask yourself: what would you like to specialize in? And from there... What tools can help you?

6. Summary and further reading

In this post, we explore what a big data engineer actually does. We take you on a tour of the field and see how big data engineering differs from data analysis and data science. We've learned that being a data engineer can make a good living, and we've shared some tips for getting your new career started!

While data engineering isn't as well known as other data-related roles, it's an in-demand job that can be very rewarding.

To learn more about what a career in data entails, tryfree 5 day short data analysis course?

We can also recommend the following:

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