What Is a Data Engineer?
Let’s first dissect the term “Data Engineer” into two: “Data” and “Engineer”. And let’s first focus on the latter part, so, what does an engineer do, well to put it simply they design and build things. “Data” engineers design and build pipelines that transform and transport data into a compatible format, for analysis by Data Scientists and other end users. These pipelines must extract data from a plethora of disparate sources and load them into a single warehouse that represents the data uniformly as a single source of truth.
Difference between Data Engineer and Scientist
As mentioned above, a data engineer is responsible for building the infrastructure and cleaning up the data for analysis by data scientists. But before data engineering was created as a separate role, all this work used to be performed by data scientists.
The separation of these two roles has largely been driven by the increasing volume and speed of data. Although there is still a certain amount of overlap between the two with respect to programming skills such as they both will likely know Python, but this doesn’t mean that the roles are interchangeable.
Since, data scientists are focused on advanced analytics of data that is generated and stored in the databases of an organization, hence why they will be well versed in math and statistics, R, algorithms, and machine learning techniques. On the other hand, data engineers design, manage and optimize the flow of data with those databases throughout the company, so they will be highly skilled in SQL, MySQL, and NoSQL, architecture and cloud technologies, and frameworks such as agile and scrum. Let’s take a detailed look at the key skills of data engineers.
Data Engineers Key Skills
- Tools and components of data architecture
Most of the required skills of data engineers are architecture-centric, as they are much more concerned with analytics infrastructure.
- In-depth knowledge of SQL and other database solutions
SQL, Cassandra, Bigtable, and so forth, are all pretty well-known database solutions. Although, SQL is important among all of these and it’s necessary for data engineers to have an in-depth knowledge of it, but knowing the others is pretty valuable too, especially if you intend to do freelancing or for hire engineering, as not every database is going to be built in the recognizable standard.
- Data warehouse architecture and ETL tools
Data Engineers need to have data warehousing experience, and as such, a strong understanding of data warehousing solutions like Redshift or Panoply is hugely valuable. Moreover, experience with ETL Tools such as StitchData or Segment along with data storage and retrieval is equally vital, as the amount of data being dealt with is very very huge.
- Hadoop based Analytics
It’s very important to have in-depth knowledge of Apache Hadoop-based analytics, along with an understanding of Hbase, Hive, and MapReduce.
Unlike, data scientists who are much better at data analytics, data engineers tend to have more advanced programming skills. Data engineers should have familiarity, if not outright expertness, with the following languages:
- and so forth.
Although it mainly comes under the work area of data scientists knowing how to act upon the data, still having some knowledge of this is invaluable for data engineers too. For this reason, it’s important for data engineers to have some understanding of statistical analysis and the data modeling basics.
- Cloud platforms
Currently, AWS is probably the most prevalent cloud skillset for data engineers to know. Google Cloud Data Engineering and Microsoft Azure are right behind.
- Various Operating Systems
UNIX and Linux knowledge is also very valuable, as many maths tools are going to be based in these systems since they require root access to hardware and OS functionality above and beyond that of Windows or macOS.
How can I become a Data Engineer?
Unlike, other traditional careers, you will have to adopt a more hybrid approach to education if you want to become a data engineer.
You will need a bachelor’s degree either in Computer Science, Software Engineering, Applied Mathematics, or IT (Information Technology), if you want to get hired as a data engineer. Now, your degree, while important, is only part of the story – getting the proper certifications can be hugely valuable too. There are a few data engineering specific certifications out there, let’s take a look at them:
- Google’s Professional Data Engineer: If you have this certificate it means you are familiar with the principles of Data Engineering and can function as either an associate or a professional in the field.
- IBM Certified Data Engineer – Big Data: Focusing on big data specific applications of Data Engineering skill sets, this is another highly valuable certificate.
- CCP Data Engineer from Cloudera: This certification shows the student is able to perform core competencies required to ingest, transform, store, and analyze data in Cloudera’s CDH environment.
- Secondary certifications, such as the MCSE (Microsoft Certified Solutions Expert), cover a variety of topics but have specific sub-certifications such as MCSE: Data Management and Analytics.
There are, of course, online hundreds of courses and studies (both free & paid) to teach you whatever you want to learn in this field. While, Udemy, EdX, and Memrise offer numerous courses in data engineering and data science, other sites, such as DataCamp, are heavily focused specifically on data science and engineering.
While these solutions are a great thing for you if you want to get started in the field, but the issue with them is that they rarely dispense certification, and at best, many only offer a certificate or diploma. So you should opt for them if you want to get your feet in the water, but you should not consider them to be a replacement for actual certification or accredited diploma issuance.
With that said I’ll wrap up this article, hope it was helpful to you.