Learn what a data engineer does, the difference between a data engineer and data scientist, data engineering key skills, and how to become a data engineer.
The demand for skilled data engineers is projected to rapidly grow. No wonder that’s the case; no matter what your company does, to succeed in today’s competitive environment, you need a robust infrastructure to both store and access your company’s data, and you need it from the very beginning.
What exactly does a data engineer do, though? And how does one become a data engineer? In this article, we’ re going to talk about this interesting field and how you can become a data engineer.
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. As part of this, Data Engineers are also responsible for the creation of data set processes used in modeling, mining, acquisition, and verification.
Engineers are expected to have a solid command of common scripting languages and tools for this purpose and are expected to use this skill set to constantly improve data quality and quantity by leveraging and improving data analytics systems.
While there is a certain amount overlap when it comes to skills and responsibilities, these two positions are being increasingly separated into distinct roles.
Data scientists are much more focused on the interaction with the data infrastructure rather than the building and maintenance thereof. They are often tasked with conducting high-level market and business operation research to identify trends and relations, and as part of this, they use a variety of sophisticated machines and methods to interact with and act upon data.
Data scientists are often well-versed in Machine Learning and advanced statistical modeling, as they are expected to take the raw data and turn it into actionable, understandable content with the help of advanced mathematical models and algorithms. This information is often used as an analysis source to tell the “bigger picture” to the decision makers.
So what makes a data scientist different from a data engineer? Generally speaking, the main difference is one of focus. Data engineers are much more focused on building infrastructure and architecture for data generation; data scientists are focused rather on advanced mathematics and statistical analysis on that generated data.
Here’s a couple of the key skills needed from data engineers.
Since data engineers are much more concerned with analytics infrastructure, most of their required skills are, predictably, architecture-centric.
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 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.
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 “put on both hats” in this case makes you a formidable tool.
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.
Data engineering typically requires a more hybrid approach to education than other, more traditional careers. While teachers often have a degree specifically in teaching, Data Engineers often have a Computer Sciences or Information Technology degree that was then further parlayed with vendor specific Certification programs and training materials.
As such, your degree, while important, is only part of the story; getting the proper certifications can be hugely valuable. There are a few data engineering-specific certifications:
There are, of course, online courses that purport to offer significant training in this field. Udemy offers numerous courses in Data Engineering and data science, and other sites, such as EdX and Memrise offer similar coursework. Some sites, such as DataCamp, are heavily focused specifically on data science and engineering, while others, such as Galvanize, are more broad-based.
While these solutions can help you get your feet in the water, so to speak, they come with the caveat that they rarely dispense or confer certification, and at best, many only offer a certificate or diploma. As such, while they are great for general learning, they should not be considered a replacement for actual certification or accredited diploma issuance.
Hopefully, this piece has illuminated the specific talents, skills, and requirements expected of a data engineer.
