What does a big data analytics engineer do?
Big data analytics engineers are responsible for creating and maintaining the analytics infrastructure that supports almost every other function in the data world. They are responsible for developing, building, maintaining, and testing big data architectures, such as databases and big data processing systems. They are also responsible for creating the processes used to model, mine, acquire, and validate data collections, among other things.
As a result, big data analytics engineers need to master common scripting languages and tools to utilize and improve data analytics systems to continually improve the quantity and quality of data.
Key Skills for Big Data Analytics Engineers
1. Tools and Components for Big Data Architecture
Big Data Analytics Engineers are more concerned with analytics infrastructure, and therefore most of the skills required are architecture-centric.
2. Deep understanding of SQL and other database solutions
Big Data Analytics Engineers need to be familiar with database management systems, and a deep understanding of SQL is critical. Likewise, other database solutions such as Cassandra or BigTable must be familiarized, as not every database is built to recognizable standards.
3. Data warehousing and ETL tools
Data warehousing and ETL experience is critical for big data analytics engineers. Data warehousing solutions like Redshift or Panoply, and ETL tools like StitchData or Segment are very useful. Additionally, data storage and data retrieval experience is equally important, as the amount of data being processed is astronomical.
4. Hadoop-based analytics (HBase, Hive, MapReduce, etc.)
Having a deep understanding of Apache Hadoop-based analytics is a very necessary requirement in this field, and a store of knowledge of HBase, Hive, and MapReduce in general is a must.
5. Coding
Speaking of solutions, coding and development skills are a major plus (and a requirement for many roles), and it would be very valuable for you to be familiar with Python, C/C++, Java, Perl, Golang or other languages.
6. Machine Learning
Machine learning has become standard data science, and knowledge of the field can help us build like-for-like solutions. This knowledge has the added benefit of making you highly marketable in the field, as being able to "wear two hats" in this case makes you a more powerful tool.
7. Multiple operating systems
Finally, it is necessary to have an in-depth knowledge of Unix, Linux, and Solaris, which are the operating systems on which many mathematical tools are based, because they have access and special hardware requirements that are not present in the Windows and Mac systems.
While these data solutions can help you get your foot in the door of big data engineering, and while they have distributions or grant certifications, they only offer certificates or diplomas. While generally learning enough, they cannot be recognized as a substitute for actual certification or practice. Hopefully, this article has shed light on the specific knowledge, skills, and requirements needed for a Big Data Analytics Engineer. The field is growing rapidly, but it is also full of challenges and perils. Filling the gaps in your skill set with the right certifications on the job is a critical step in realizing the best learning. In summary, this is what I have shared with you today, and I hope it helps.