No matter what company does, in order to succeed in today's competitive environment, you need a robust infrastructure to both store and access the company's data. And, it needs to be done from the very beginning. Thus the demand for skilled data engineers is rapidly growing and is projected to grow at a faster rate.
Data engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance, and testing of architectures, such as databases and large-scale processing systems. They are also responsible for creating data set processes used in modeling, mining, acquisition, and verification.
The data engineers are expected to have a solid command over common scripting languages and tools for constantly improve data quality and quantity by leveraging and improving data analytics systems.
Some Key skills required for becoming a Data Engineer are:
- Tools and Components of Data Architecture
Since data engineers are much more concerned with analytics infrastructure, most of their required skills are, predictably, architecture-centric.
- In-Depth Knowledge of SQL and Other Database Solutions
Data Engineers need to understand database management, and as such, in-depth knowledge of SQL is hugely valuable. Likewise, other database solutions, such as Cassandra or Bigtable, are great to know if you plan on doing freelance or for hire engineering, as not every database is going to be built in the recognizable standard.
- Data Warehousing and ETL Tools
Data warehousing and ETL experience is essential to this position. Data warehousing solutions like Redshift or Panoply, as well as familiarity with ETL Tools, such as with StitchData or Segment is hugely valuable. Similarly, experience with data storage and retrieval is equally vital, as the amount of data being dealt with is simply astronomical.
- Hadoop-Based Analytics (HBase, Hive, MapReduce, etc.)
Having a strong understanding of Apache Hadoop-based analytics is a very common requirement in this space, with knowledge of HBase, Hive, and MapReduce often considered a requirement.
Speaking of solutions, knowledge of coding is a definite plus here (and also possibly a requirement for many positions). Familiarity, if not outright expertness, is very valuable in Python, C/C++, Java, Perl, Golang, or other such languages.
While mainly the focus of data scientist, some level of understanding of how to act upon this data is also invaluable for Data Engineers. For this reason, some knowledge of statistical analysis and the basics data modeling are hugely valuable.
While machine learning is technically something relegated to the Data Scientist, knowledge in this area is helpful to construct solutions usable by your cohorts. This knowledge has the added benefit of making you extremely marketable in this space, as being able to ├?┬ó??put on both hats├?┬ó?? in this case makes you a formidable tool.
- Various Operating Systems
Finally, intimate knowledge of UNIX, Linux, and Solaris is very helpful, as many math tools are going to be based in these systems due to their unique demands for root access to hardware and operating system functionality above and beyond that of Microsoft's Windows or Mac OS.
As this field is unexpectedly growing, it is also not free from a number of obstacles. Therefore, attaining the best education possible while filling any gaps in skill sets with proper certification is key to become a well-qualified data engineer.