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Big data engineers solve complex challenges in a strategic manner. They design and maintain data infrastructure and tools, which include gathering, storing, processing, and analyzing a variety of data and data systems. Big data engineers know how to work fast and correctly while analyzing large data sets with the best technologies available, and they know how to generate results.
Skill | Why it’s important |
Machine Learning | Machine learning (ML) is an important tool for big data engineers because it helps them to quickly organize and handle massive volumes of data. In addition, because machine learning algorithms "learn" by analyzing data sets, big data is a component of their development. Engineers should be conversant with the process of creating machine learning algorithms. They must be able to develop them and employ algorithms in the data import process. |
Hadoop | Hadoop is a set of open-source tools that can simultaneously handle enormous data volumes across thousands of servers and devices. Depending on the data and mode it operates in, it has variable degrees of the scale. Data engineers should be aware of the various modes and how they are used. They should also be aware of the technologies accessible to them and the situations in which Hadoop may be used. |
Java | Because of its efficiency and object-oriented design, Java is one of the most extensively used coding languages. It's also one of the most widely used languages for developing data sorting algorithms and machine learning algorithms. This is why it is such an important talent for big data engineers. This is a language that engineers should be familiar with. They should also be conversant with Java machine learning libraries such as Java ML and know how to develop automated scripts. |
Python | Python is a popular programming language because of its flexibility. Python has a large community and a large number of libraries. As a result, engineers should not only be familiar with Python and how to create tools with it but also be active in contributing to and using Python libraries. |
Java | Because of its efficiency and object-oriented design, Java is one of the most extensively used coding languages. It's also one of the most widely used languages for developing data sorting algorithms and machine learning algorithms. This is why it is such an important talent for big data engineers. This is a language that engineers should be familiar with. They should also be conversant with Java machine learning libraries such as Java ML and know how to develop automated scripts. |
To create and manage our company’s big data solutions, we’re searching for a professional Big Data Engineer. You will be responsible for designing and implementing big data tools and frameworks, implementing ELT procedures, collaborating with development teams, building cloud platforms, and maintaining the production system in this position.
You should have an in-depth understanding of Hadoop technology, great project management abilities, and high-level problem-solving skills to succeed as a big data engineer. A top-notch big data engineer comprehends the company’s requirements and implements scalable data solutions to meet those requirements now and in the future.
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You can use these sample interview questions for Big Data Engineer once you’ve gathered all of the applications. These questions might ease the rest of your hiring journey.
Personal
Human Resources
Management
Technical Skills and Knowledge
Most big data engineers have a bachelor’s degree and others have a master’s degree, such as an online master’s in business data analytics. The additional years of education are critical for understanding the wide range of technical abilities required of a big data engineer.
Big data engineers usually earn between $76,000 to $168,500 per year, and their median annual salary is around $130,674. The hourly wages go from $39 to $81, and the median hourly pay is $63.
Percentile | 10% | 25% | 50% (Median) | 75% | 90% |
Hourly Wage | $39 | $54 | $63 | $71 | $81 |
Annual Wage | $76,000 | $111,500 | $130,674 | $148,500 | $168,500 |
The primary distinction is the emphasis area. A data scientist is concerned with sophisticated mathematics and statistical analysis, whereas a data engineer is concerned with data creation infrastructure and architecture. To put it another way, the data scientist is concerned with answering questions, whereas the data engineer is concerned with supplying data.
According to IBM (2021), data engineers will see a 28 percent increase in job opportunities over the next three years. Finance, IT, insurance, and professional services will account for nearly 60% of all jobs.
You’ll require a strong understanding of programming languages and technologies like SQL, Cassandra, and Bigtable. You’ll need to know how to set up data-generating infrastructure and architecture. You should also be familiar with ETL and other data warehouse architecture.
Knowledge of Hadoop-based analytics is beneficial, and coding skills will give you an edge. Finally, familiarity with multiple operating systems such as UNIX and Linux is essential.
Several websites, such as Udemy and EdX, provide data engineering courses if you want to try them out. While you won’t generally be able to get certified this way, it is a wonderful method to put your skills to the test.