Big Data engineers must have both education and work experience, and many choose to pursue certification. Learn about the duties of big data engineers, potential career paths, and how to get started.

This article will outline each essential component of being a big data engineer. After reading it, you should know more now about whether or not this is the right career for you.

Big Data Engineer: Definition

A Big Data Engineer is a professional responsible for designing, building, and maintaining the infrastructure and systems needed to store, process, and analyse large and complex data sets. This includes designing and implementing data storage solutions, creating and optimising data pipelines, and developing and deploying big data processing frameworks. Big Data Engineers also work closely with data scientists, data analysts, and other stakeholders to understand their data needs and help them extract insights and value from the data.

Typical job responsibilities of a Big Data Engineer

Some specific job responsibilities for a Big Data Engineer include the following:

• Designing and building large-scale data storage systems, such as distributed file systems and NoSQL databases, to store and manage big data.
• Developing and implementing data pipelines and ETL processes to extract, transform, and load data from various sources into the data storage systems.
• Writing code to process, analyse, and visualise big data using programming languages such as Python, Java, and Scala and big data processing frameworks such as Apache Hadoop, Apache Spark, and Apache Storm.
• Collaborating with data scientists, data analysts, and other stakeholders to understand their data needs and help them extract insights and value from the data.
• Monitoring and optimising the performance of the data storage and processing systems and troubleshooting any issues that may arise.
• Keeping up to date with the latest developments in big data technologies and trends and incorporating new tools and techniques into the data infrastructure as needed.
• Knowing Cloud technologies such as AWS, GCP, Azure etc.
• Knowing containerisation and orchestration technologies such as Docker and Kubernetes.
• Knowing software engineering best practices such as version control, continuous integration, and testing.

Big data engineer vs data scientist

Big Data Engineers and Data Scientists work with big data, but their roles and responsibilities differ.

Big Data Engineers focus on the technical aspects of handling and processing big data. They design, build and maintain the infrastructure to store and process data sets. They also work closely with data scientists, data analysts, and other stakeholders to understand their data needs and extract insights and value from the data.

Data Scientists focus on analysing and interpreting big data. They use statistical and machine learning techniques to extract insights and find patterns in large data sets. They also use visualisation and communication to present their findings to stakeholders clearly and promptly. Data Scientists also work with Big Data Engineers to get the data they need and ensure the infrastructure is set up to handle the data.

While both Big Data Engineers and Data Scientists are essential for working with big data, their roles tend to be more distinct and separate, with Big Data Engineers focusing on the technical side of things and Data Scientists focusing on the analytical side.

In-demand skills for Big Data Engineers

In-demand skills for Big Data Engineers include:

• Strong programming skills in Java, Python, or Scala, and experience with big data processing frameworks such as Apache Hadoop, Apache Spark, and Apache Storm.
• Experience with distributed and storage systems such as HDFS, Hbase, and Apache Cassandra.
• Experience with NoSQL databases such as MongoDB, Cassandra, and Hbase.
• Experience with data pipeline and ETL tools such as Apache NiFi, Apache Kafka, and Apache Flume.
• Strong understanding of data modelling, data warehousing, and data governance concepts.
• Experience with Cloud technologies such as AWS, GCP, Azure, etc.
• Experience with containerisation and orchestration technologies such as Docker and Kubernetes.
• Experience with software engineering best practices such as version control, continuous integration, and testing.
• Strong analytical and problem-solving skills and the ability to work with large and complex data sets.
• Familiarity with machine learning and statistical techniques and understanding how to apply them to big data.
• Strong communication and collaboration skills to work with data scientists, data analysts, and other stakeholders to understand their data needs and help them extract insights and value from the data.
• Knowing security, governance, and compliance requirements for extensive data systems
• Experience with big data visualisation tools such as Tableau, QlikView, and Power BI.

How to become a Big Data Engineer

Becoming a Big Data Engineer typically requires a combination of education, experience, and skills. Here are some steps you can take to become a Big Data Engineer:

• Get a degree in a relevant field such as computer science, engineering, or mathematics. A degree in these fields will provide a strong foundation in programming, data structures, and algorithms, which are essential skills for a Big Data Engineer.
• Gain experience with big data technologies and frameworks. This can be done through internships, personal projects, or by working on open-source projects.
• Learn programming languages such as Java, Python, and Scala and big data processing frameworks such as Apache Hadoop, Apache Spark, and Apache Storm.
• Get experience with distributed systems, distributed storage systems, and NoSQL databases.
• Learn data pipeline and ETL tools like Apache NiFi, Apache Kafka, and Apache Flume
• Gain knowledge of Cloud technologies such as AWS, GCP, Azure, etc.
• Learn containerisation and orchestration technologies such as Docker and Kubernetes.
• Get familiar with software engineering best practices such as version control, continuous integration, and testing.
• Learn data modelling, data warehousing, and data governance concepts.
• Gain experience with machine learning and statistical techniques.
• Develop strong analytical and problem-solving skills.
• Develop good communication and collaboration skills to work with data scientists, analysts, and other stakeholders.
• Get professional certifications to demonstrate your skills and knowledge in big data engineering.
• Continuously update your knowledge and skills by staying current with the latest developments and trends in big data technologies.

It's also important to note that, like many other fields, gaining experience through real projects and real-world applications will be essential to demonstrating your abilities and applying your knowledge in practical situations.

Big Data Engineer: Career and Job Scope

As a Big Data Engineer, you can expect to work in various industries such as technology, finance, healthcare, retail, and more. 

Your primary responsibility would be to design, build, and maintain the systems and infrastructure needed to store, process, and analyse large and complex data sets. 

This can include designing data storage solutions, creating data pipelines, and developing big data processing frameworks.

The demand for Big Data Engineers is growing as the amount of data generated continues to increase. 

Many organisations seek individuals to help them extract insights and value from their data. This can lead to several job opportunities for Big Data Engineers, including roles such as:

• Big Data Engineer
• Senior Big Data Engineer
• Lead Big Data Engineer
• Principal Big Data Engineer
• Big Data Infrastructure Engineer
• Big Data Platform Engineer

As a Big Data Engineer, you can expect to work in a team environment, collaborating with data scientists, data analysts, and other stakeholders to understand their data needs and help them extract insights and value from the data. 

You can also expect to work with the latest technologies and tools and constantly learn and adapt to new developments in the field.

The salary of a Big Data Engineer varies depending on experience, location and the type of company they work for. According to payscale.com, the average salary for Big Data Engineers is around $110,000 annually and can go up to $150,000 or more for experienced engineers with in-demand skills.

Overall, a career as a Big Data Engineer offers a challenging and exciting opportunity to work with cutting-edge technologies and large data sets to extract insights and value for organisations. 

With the increasing demand for big data expertise, this field will likely continue to grow and offer many job opportunities for those with the right skills and experience.