Wondering what is a big data engineer? As the name suggests, Big Data is associated with ‘big’ data, which hints at something big in the context of data. Big data forms one of the pillars of data science. Big data has been a hot topic in the IT sector for quite a long time. Every big company is either eager to implement big data analytics into their business strategies or has already incorporated it into their systems.
These large volumes of data are helpful for companies in any sector as nowadays, user data shares equal importance in a company alongside its profits and market share. Technology and business strategies go hand in hand, and data analytics is no exception. The question is, who manages all this data, and how do they do it? Let me introduce you to who is big data engineer and what does a big data engineer do.
Who Is a Big Data Engineer?
A big data engineer is often confused with a data engineer because they work with data. To answer the question of the work of big data engineers, they handle large and more complex data more efficiently. They are responsible for analyzing, testing, and maintaining the large workloads in the company. The role of the big data engineer comes into the picture once the data is properly collected, processed, and stored by a data engineer.
The big data engineer then analyzes this data using unique algorithms and data models to gain valuable insights.
What Does A Big Data Engineer Do? [Roles and Responsibilities]
What does a big data engineer do? A big data engineer is crucial to any company’s data management team. While working as a big data engineer, there are some roles and responsibilities one has to do:
- Designing large data systems starts with designing a capable system that can handle large workloads. It includes designing the data pipelines and APIs and designing data architectures on software and hardware levels to meet the business’s requirements.
- Develop the algorithms: Once the database is ready, the next thing is to analyze the data to obtain valuable insights. This is done by specific data analyzing algorithms implemented into the data models to analyze the data efficiently.
- Maintenance: Bugs are common when dealing with different sizes and types of datasets. These bugs need to be addressed to ensure that the system is always up and running.
- Adapt and Improve: As new and efficient techniques emerge in the field of data analytics, they need to be adapted and implemented to keep up with the latest trends and increase the system's efficiency.
Characteristics of a Big Data Engineer
Big data engineers don’t possess any superpowers to handle big data. So, what do big data engineers do? They develop skills that can be achieved by any individual with enough practice:
- Problem-solving skills: Big data is about solving the problem and obtaining optimized and well-structured information from the dataset. To achieve that, one must be thorough with data problems and techniques, which can be mastered by starting with small datasets and gradually increasing the volume.
- Adaptable: As I have mentioned earlier, big data engineers should be open to changes, ready and capable of adopting new techniques and frameworks to keep developing a better version of themselves.
- Love for learning: Any engineer should have the hunger to learn new things. Adapting new tech because of peer pressure is useless and will end up being nothing more than a job burden. The love for learning new things is enough to drive engineers to stay updated with the market.
- Wide range of knowledge: Big data engineers must deal with problems requiring knowledge from various fields. It is often said that big data engineers should have both depth and width in their knowledge.
- Technical expertise: Big data engineers should be thorough in their knowledge of technical fields such as programming languages, such as Java and Python, database management tools like SQL, frameworks like Hadoop, and machine learning.
Qualification and Required Skills for a Big Data Engineer
The skills required for big data engineers can be achieved through quality education and certifications. To fit into the role of a big data engineer, one must meet the following big data engineer requirements:
- Master’s or Bachelor’s degree in computer science or similar field
- The role of a Big Data engineer is crucial in a company. Thus, the role demands prior experience in handling large volumes of data.
- To ensure the datasets are correctly handled, the Big Data Engineer should be thorough with various ETL tools, SQL tools, frameworks like Hadoop and Apache Spark, and programming languages like Python or Java.
- Apart from the above-mentioned big data engineer qualifications, they should also be able to convey their ideas effectively to collaborate with different business teams.
Education, Training, and Certifications
Anyone can achieve the skills possessed by an excellent big data engineer with practice and training. Here are a few big data engineer education requirements:
- Bachelor’s or Master’s degree: A degree from a credible institution can help build a strong foundation that includes basic problem-solving skills, programming skills, etc., which will play an essential role in your career journey.
- Technical programs for training: The best online Data Structure courses are a backup for your skills, especially if trusted sources like NPTEL or KnowledgeHut grant them. Such technical courses allow you to explore big data in depth, Master advanced concepts, and massively boost your career by helping you secure a reputed job in your early days.
- Real-world experiences: The skill-development courses for big data technology offered by trusted platforms like KnowledgeHut focus on providing real-world experiences to the candidates with the help of real-world projects in Hadoop systems that you can flaunt in your portfolio.
- Gaining certifications: Certifications might seem basic, but they can provide you with the upper hand regarding interviews. Enrolling yourself in technical programs like KnowledgeHut’s Big Data Analytics course and technical programs in high demand will help you upskill yourself in the field of Data analytics, especially in Big Data.
Big Data Engineer as a Career
The skills required to become a practical big data engineer make them more versatile and valuable in any industry. Anyone with the earlier-mentioned skills and certifications can work as a successful big data engineer while fitting themselves into various job roles. Here are a few job roles suitable for a big data engineer:
1. Data Architect
Big data engineers develop software systems that handle large loads of data. In any company that has to do with large databases, Big Data engineers can easily fit into the role of a Data Architect.
Salary: $135,000 - $165,000
2. Big Data Analyst
Big data engineers can also become influential data analysts skilled at handling large datasets, structured or unstructured.
Salary: $105,000 - $125,000
3. Data Warehouse Engineer
Along with developing data systems, a big data engineer’s roles and responsibilities include maintaining those systems. Big data engineers can act as Data Warehouse engineers to bring consistency to the system.
Salary: $105,000 - $ 110,000
Big Data Engineer Job Growth and Salary
Job opportunities for big data engineers continue to grow as the new technology enters business strategies. Data will play an essential role in deciding business models and government policies.
Here is the list of the salaries offered to big data engineers in the top 5 countries worldwide.
Top Countries | Average Salary |
USA | $110,000 |
UK | $65,000 |
Australia | $120,000 |
Canada | $80, 000 |
Germany | $65,000 |
Apart from location-specific benefits, several tech giants are also ready to invest in skilled data engineers. Here is the list of the top 10 companies hiring big data engineers across the globe:
Company | Average Salary |
Google | $160,000 |
Amazon | $130,000 |
Accenture | $105,000 |
Meta | $150,000 |
Netflix | $180,000 |
Uber | $150,000 |
Adobe | $140,000 |
JPMorgan Chase | $105,000 |
Microsoft | $150,000 |
Apple | $150,000 |
Big Data Engineer: Job Satisfaction
Job satisfaction solely relies on how passionate you are and how much you like your job. If you are stuck in a job that is not your choice, even if the circumstances might be favorable, you may not be satisfied with your job and vice versa.
However, certain factors make the job favorable and give the employee a feeling of satisfaction, like career growth, work-life balance, etc. While the career opportunities in data engineering may be immense, the work-life balance can sometimes fall below average. This is due to constant workloads that might not be stressful but are time-consuming, which can disrupt the work schedule.
How to Become a Data Engineer?
The steps to becoming a big data engineer include gaining a full-fledged degree and certifications in specializations.
- Earn a degree: The first step is to get a professional degree in computer science or something similar, equivalent to hard work and education.
- Learn through work experience: Apart from learning during the degree program, you must also get some work experience to remove the fresher tag hovering over future job opportunities.
- Certification: Though you have earned a degree and some work experience, certification from recognized platforms like KnowledgeHut’s Coding Bootcamp for Software Engineers is like a cherry on top. While competing for jobs in the industry, certifications can give you an advantage over other candidates.
Challenges Faced by Big Data Engineers
1. Managing multiple data systems
While working for large companies, big data might have to deal with multiple database systems, each with its own algorithms, data models, etc.
This challenge can be easily fixed by standardizing the data manipulation tools to make operating the data on multiple systems easy.
2. Data storage challenges
As big data grows, the first challenge arises to accommodate the constantly increasing data seamlessly and efficiently, while the second challenge is to retrieve the data efficiently.
This can be solved by partitioning the data into multiple systems and choosing the proper data storage platform, considering the system’s scalability and performance.
3. Processing speeds
The data processing speeds depend on the performance of the machines on which they are being run. Sometimes, the volume of data can outperform the machines and increase the processing times, leading to inefficiency.
This can be resolved by shifting towards the cloud and other faster data storage techniques, leading to faster data retrieval times, etc.
Conclusion
The importance of a big data engineer in any business is higher than before, as data has become a core part of business policies in today’s modern era. Big Data engineers are required in every field related to data management. Individuals passionate about Big Data can prepare themselves through degree courses and certification programs offered by platforms like KnowledgeHut.
Big data is a field that requires deep knowledge of Data science and machine learning, which makes it challenging to master. However, once mastered, it can lead to highly lucrative jobs with the right salary and an outstanding work-life balance.
