How to Become a Big Data Engineer in 2024 [Step-By-Step Guide]

Big data engineer

Since efficiency and creativity are substantially enhanced by technology, a big data engineer plays a unique position in the ever-changing world of technology. In 2024 and beyond, there will be an increasing demand for big data specialists. This comprehensive article will help you navigate the challenging career path of a Big Data Engineer by providing invaluable insights into the core skills, educational programs, and real-world job experience needed to succeed in this fascinating field.

We’ll look at the newest techniques, tools, and advancements together to make sure you succeed in the quickly developing data science industry. This guide is meant to assist you, irrespective of your degree of expertise or data interest, in building a rewarding career in extensive data engineering.

Big Data: What is It?

Before we can fully understand the roles and responsibilities of a big data engineer, we must first understand what big data is. BLS, or The U.S. Bureau of Labor Statistics, defines big data as the unprecedented amount of information that companies are producing and analyzing. Most of this data is sourced from smartphones, e-commerce websites, and social media platforms.

Big data colleges

The phrase “big data” is still in its infancy and evolving. Therefore, understanding and classification of this is still underway. But big data may also be used to predict market movements, industry changes, and other trends exceptionally accurately. Big data goes beyond the immediate information and looks at a much bigger picture.

Who is a Big Data Engineer?

Big data engineers are the people who handle and manage these extensive datasets to produce valuable insights. These big data engineers are also known as statisticians, data scientists, and computer and information research scientists.

Big data engineers offer insights to businesses that aid in performance evaluation, market demographic identification, and future change and trend forecasting. Because big data engineers generate insights that are beneficial in business, government, finance, telecommunications, science, healthcare, and other industries, they are needed in almost every field.

Roles and Responsibility of Big Data Engineers

There is a lot a big data engineer does besides interacting with databases and massive data processing systems. They are also responsible for sorting the data to find relevant datasets for analysis purposes, used by companies to predict behaviors. Below are some other roles and responsibilities of big data engineers:

  • They work alongside IT teams and data architects to formulate project goals.
  • Construct highly scalable data management solutions from conception to fulfillment.
  • Design and develop predictive models, top-tier algorithms, and prototypes.
  • Design dataset processes that can be used in data mining, modeling, and production.
  • Develop analytics apps and other custom software.
  • Make sure that all data systems meet the required specifications.
  • Supervise the preparations for catastrophe recovery.
  • Enhancements to the quality, dependability, and effectiveness of research data.
  • Seek new applications for current tools and data, as well as chances to acquire new data.

Steps to Become a Big Data Engineer

Using this detailed tutorial, let’s learn how to become a big data engineer:

1.Get a Degree

If you want to start your big data engineer career, even from an entry-level profile, you will need a degree. These degrees can be obtained in disciplines like computer science, applied math, software engineering, or physics. A degree will provide you with not only a strong foundation but also the right soft skills needed for your role. Among other soft skills, you will learn about problem-solving, communication, and teamwork.

Colleges and universities now provide degrees with the opportunity to obtain real-world experience. It helps the students to gain real-world exposure before starting their professional journey.

2.Consider a Certification Course

A course is a great way to start a career as a big data engineer, especially if you are already in some other field or have a degree. You can take an intensive 12-week online Data Analytics and Engineering bootcamp training program from CCSLA to fast-track your career in this field. These bootcamp programs are less expensive when compared to a full-time degree and are often short.

Based on the type of course you prefer, you can learn different skills, such as machine learning, big data architecture, or data analytics, to name a few. In addition, these courses will also equip you with skills and programming languages like SQL and Python needed to succeed in the job. It is also an excellent option for people who are working full-time and want to begin their careers as big data engineers.

3.Gather Technical Skills

Being a big data engineer does not only mean getting a degree or course; you also need to excel in technical knowledge. Some technical skills that one must know and learn are:

Database Systems

Everything revolves around data in the big data engineer’s world. From handling, processing, and managing, these engineers have to take care of everything. Hence, it would help if you had a good understanding of NoSQL and SQL databases. Most companies use SQL as their database system. Similarly, NoSQL databases are also becoming popular as they offer more capabilities.

With these database systems, you should also know how to create databases, manipulate databases, and operate on other data sets.

Data Warehousing

For storing and analyzing data, you will need various data warehouses. Companies use internal and external sources to gather and collect the data. Hence, you should be aware of warehouses, such as Amazon Web Services (AWS) and its entire concept.

Machine Learning

When considering big data, one needs to understand that artificial intelligence and machine learning also go hand in hand. To predict data, you will need to master machine learning. With this ML knowledge, you will be able to communicate and collaborate with data scientists and engineering teams. Moreover, you will be able to build a better data pipeline with ML algorithms.

ETL Tools

ETL means extracting, transferring, and load-pulling data from various sources. It also means applying specific rules per business requirements on the different datasets. After that, the converted data is loaded for usage in the database or other BI platforms. You will utilize ETL a lot as a data engineer to maintain the data pipelines. You ought to be aware of the procedure and the available resources.

Programming Skills

Even though programming skills or languages may differ from company to company, one must know the basics or the most commonly used. Some programming languages you should know are R, Python, Java, C#, C++, etc.

Data Structures and Algorithms

You must comprehend the data structures and functionalities used by different organizations. To define checkpoints and manage big data frameworks efficiently, a basic understanding of algorithm design and data structures is also required.

Big Data Technologies

It will be a great idea to gain some understanding or knowledge about big data technologies like Spark and Hadoop. Such tools are needed to process large datasets which are distributed across various machines. Hence, they are essential for a data engineer to know and understand.

4.Develop Soft Skills

Soft skills are equally crucial for a big data engineer’s journey, just as technical skills have a specific position. As a result, it makes sense to practice abilities like problem-solving and creative thinking. This is because you will typically be working with complicated datasets and will need to apply your troubleshooting abilities to come up with an original solution.

5. Hands-on Experience

An added advantage will be to get hands-on experience with data. You can take projects or internships that involve handling real-world datasets and their related situations. It will offer you the correct exposure needed to become a big data engineer.

6.Staying Updated

Whatever the industry, it’s always important to stay current with new advancements. It becomes more essential for big data engineers as this field is still developing, and now and then, there are new releases and information around big data.

7.Build Your Resume

After earning a degree or certification, the next step in learning how to become a big data engineer is to create a compelling CV. This is the most crucial stage in achieving one’s ideal job. A CV needs to be concise, official, and eye-catching.

All relevant information, including employment history, hobbies, educational background, specialized certifications and skills, and internship experience, must be included.

Job Outlook and Salary of Big Data Engineers

Big data engineers are in great demand, and as per Glassdoor, their average salary is $1,53,357. With experience, big data engineers can even make more than the average amount. However, you need to remember that people joining this field at entry levels may earn less than the average as well.

The US Bureau of Labor Statistics classifies big data engineers as statisticians and computer and information research scientists. Their employment prospects are as follows:

A growth rate of thirty percent is anticipated, above the average growth rate between 2022 and 2032. Scientists working in computer and information research are predicted to expand by 23%, which is more than the average growth rate between 2022 and 2032.

How Do Big Data Engineers Fit in Various Companies?

1.Small Size Companies

In a small team or company, a data engineer could be in charge of all aspects of the data flow. They can be asked to set up data sources to oversee analytical tools. Stated differently, they would create, build, and oversee databases, data warehouses, and data pipelines in the same manner as a full-stack data scientist.

2.Mid Size Companies

Data scientists and big data engineers collaborate to create any specialized tools required to meet specific big data analytics objectives in a mid-sized business. They are in charge of the data integration technologies that link data sources and data warehouses. These pipelines can do more specialized functions or move data from one location to another.

3.Large Size Companies

A typical job specification for a big data engineer at a large company with complex data demands calls for the creation of table schemas, optimization of analytics databases for quick analysis, and setup and populating of analytics databases.

The process of taking data (extracted) from a source, transforming it (transformed) into a type that can be analyzed, and then storing it (loaded) in a data warehouse is known as ETL (Extract, Transfer, Load) work. It is among the primary jobs for a big data engineer in large companies.

How to Increase Your Chances of Getting a Big Data Engineer Job?

One of the most essential elements that help increase the job percentage is continuous learning. People often feel that after completing the course, they are equipped and prepared for working as a big data engineer.

However, looking at the demand and competition, the right approach will be to sharpen your skills with as much expertise as possible. If you have completed a degree, go for an industry-recognized certification in data analytics or a relevant bootcamp training program to fast forward your career.

Moreover, you can also go for higher education if needed. You could choose to pursue a master’s degree, for example, if you currently hold a bachelor’s degree. If you follow this, you’ll always be one step ahead of the competition.

In addition to academic credentials, practical experience in the workplace will be essential to expanding your career prospects. Companies these days are looking for people with prior experience or project work. Moreover, they are also looking for candidates with a strong command over programming and soft skills. Hence, you should create as many personal projects as possible and practice your coding skills.

Lastly, don’t expect too much out of your first interview. You may have all the skills but need more experience for the job. Hence, you may only get an entry-level job. Once you get a job, even as an entry-level, it will add to your work experience and knowledge. Moreover, you will acquire a lot of valuable insights into how the big data field works. All of these can be beneficial for your future job prospects.


With the help of this guide, you may envision yourself having the skills, confidence, and know-how required to thrive in the exciting and quickly evolving big data industry. By sticking to your dedication to lifelong learning, embracing cutting-edge technologies, and honing your talents, you can set yourself up for success. The subject of data science is dynamic and full of opportunities, so keep that in mind whether you want to start a new career or develop your existing abilities.

If you are looking to start your career in this field, consider joining the data analytics and engineering bootcamp by CCLSA. This is one of the best intensive training programs to help you prepare for a successful job in big data engineering. The best aspect is that you can prepare for a long-term career by completing this online in just 12 weeks.


Q1: What is a Big Data Engineer?

Answer: A Big Data Engineer is a professional who designs, builds, and manages the infrastructure and tools that allow businesses to collect, store, process, and analyze vast amounts of data (big data) efficiently. They work with complex datasets to provide insights that can influence business decisions and strategies.

Q2: What educational background is required to become a Big Data Engineer?

Answer: Typically, a bachelor’s degree in computer science, information technology, engineering, or a related field is required. However, individuals with degrees in mathematics or statistics can also enter this field if they possess strong programming and data management skills.

Q3: Which programming languages are essential for a Big Data Engineer?

Answer: Proficiency in Java, Scala, and Python is crucial for Big Data Engineers. Knowledge of SQL for database management and R for statistical analysis can also be beneficial.

Q4: What are the key skills needed to be a successful Big Data Engineer?

Answer: Key skills include expertise in big data technologies like Hadoop and Spark, experience with database management systems, strong analytical and problem-solving abilities, knowledge of cloud computing platforms like AWS or Azure, and proficiency in data warehousing solutions.

Q5: How can I gain practical experience in Big Data Engineering?

Answer: Practical experience can be gained through internships, working on personal or open-source projects, participating in hackathons or competitions focused on big data challenges, and contributing to big data projects in your current job if applicable.

Q6: What is the role of machine learning in Big Data Engineering?

Answer: Machine learning plays a significant role in Big Data Engineering, as it allows for the analysis and modeling of complex datasets to predict trends, automate decision-making processes, and create intelligent applications. Big Data Engineers need to understand machine learning algorithms and how to implement them at scale.

Q7: Can I become a Big Data Engineer without a background in computer science?

Answer: While challenging, it is possible to become a Big Data Engineer without a traditional computer science background. Focusing on acquiring the necessary technical skills through online courses, bootcamps, and hands-on projects, as well as gaining a solid understanding of data systems and analytics, can facilitate this transition.

Q8: What industries hire Big Data Engineers?

Answer: Industries that hire Big Data Engineers include technology, finance, healthcare, telecommunications, retail, and government. Any industry that deals with large volumes of data and requires insights from that data to make informed decisions is likely to hire Big Data Engineers.

Q9: How important are communication skills for a Big Data Engineer?

Answer: Communication skills are very important for Big Data Engineers, as they often need to explain complex data concepts and the significance of their findings to non-technical stakeholders. Effective communication can bridge the gap between technical execution and business strategy.

Q10: What are the career advancement opportunities for Big Data Engineers?

Answer: Career advancement opportunities for Big Data Engineers include moving into senior engineering positions, specializing in areas like machine learning or data science, or transitioning into managerial roles such as leading a data engineering team or becoming a Chief Data Officer (CDO).

Q11: How do I stay updated with the latest trends and technologies in Big Data Engineering?

Answer: Staying updated involves following industry news, participating in professional networks and forums, attending workshops and conferences, and continuously exploring new tools, algorithms, and methodologies in big data and analytics.