How to become a Data Engineer – A complete career guide
Data engineers are necessary in the big data revolution to build, test, and maintain data architecture. Closely linked with data architects—indeed, these two positions must collaborate on most projects—data engineers focus on the construction of systems that can house massive amounts of data. The architecture that a data engineer builds allows a data scientist to easily pull relevant data sets for analysis.
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|School Name||Level||Program||More Info|
|Georgetown University||Master||Master of Science in Business Analytics||website|
|Johns Hopkins University||Master||Online MS in Data Analytics and Policy||website|
|Utica College||Master||Online MS in Data Science||website|
|Husson University||Bachelor||B.S. in Data Analytics||website|
|Capella University||Bachelor||B.S. in Information Technology||website|
|Southern New Hampshire University||Bachelor||B.S. in Data Analytics||website|
|George Mason University||Master||Online MS in Data Analytics Engineering||website|
|Drake University||Master||Master of Science in Business Analytics||website|
|Saint Joseph’s University||Master||Master of Science in Business Intelligence and Analytics||website|
|Northern Illinois University||Master||Online Master of Science in Data Analytics||website|
Five steps to launching a successful Data Engineer career
Step 1: Earn your undergraduate degree
The best majors include software engineering, computer science, or information technology. As this job requires more engineering than math or science, alternate possibilities are related to engineering. Regardless of your major, make sure to take courses in software design, computer programming, data architecture, data structures, and database management.
Step 2: Gain entry-level job experience
An easy way to gain entry into the career of data engineer is to seek out IT assistant positions, whether at your college or at a small company. Hone your skills in computer programming and software design, as strong fluency in many programming languages will be necessary for your career. As you gain experience, begin to solve real-world problems by choosing public data sets and build a system end-to-end. This experience will be necessary to prove to employers that you have the hard skills and the tenacity to be a data engineer.
Step 3: Get your first job as a data engineer
Companies around the world are hiring data engineers to develop their data infrastructure. In particular, look for positions at software corporations, computer manufacturers, and computer system design companies. This will allow you excellent mentorship and guidance, as well as projects at the front lines of data science. Unsurprisingly, Silicon Valley has one of the highest concentrations of data engineer jobs in the country.
Step 4: Obtain professional certifications
There are a number of industry certifications available to data engineers. One popular and well-known option, offered by the Institute for Certified Computing Professionals (ICCP), is the Certified Data Management Professional (CDMP) credential. You can get this certification either at the “practitioner” or “mastery” level. Other certifications include Google’s Certified Professional in data engineering, IBM Certified Data Engineer in big data, the CCP Data Engineer from Cloudera, and the Microsoft Certified Solutions Expert credential in data management and analytics.
Step 5: Pursue a higher degree
As you progress in your career, you may also want to pursue a master’s in computer science or computer engineering. However, data engineering is not as academically focused as, data science, and thus many data engineers succeed with strong design and programming skills, but no advanced degree. A Ph.D. is generally not required for jobs in data engineering.
What is a Data Engineer?
Data engineers build and maintain data pipelines, warehousing big data in such a way that makes it accessible later on. This infrastructure is necessary for every other aspect of data science. The data engineer develops, constructs, maintains, and tests architecture, including databases and large-scale processing systems. The data set processes that data engineers build are then used in modeling, mining, acquisition, and verification.
The data engineer works in tandem with data architects, data analysts, and data scientists. Data architects are in charge of data management systems, and understand a company’s data use, while data analysts interpret data to develop actionable insights. Finally, data scientists focus on machine learning and advanced statistical modeling. They must share these insights to other stakeholders in the company through data visualization and storytelling.
What does a Data Engineer do?
The data engineer is chiefly in charge of designing, building, testing, and maintaining data management systems. This allows the generation of applicable data for specific projects. To do this, data engineers must have a strong command of common scripting languages. They must solve complex problems on a coding level.
Note that data engineers are the builders of data systems, and not those who mine it for insights. The data engineer thus works more “behind-the-scenes” and must be comfortable with other members of the team producing business solutions from this data.
Data Engineer job description
- Implement, verify, design, and maintain software systems
- Build data architecture for ingestion, processing, and surfacing of data for large-scale applications
- Extract data from one database and load it into another
- Use many different scripting languages, understanding the nuances and benefits of each, to combine systems
- Research and discover new methods to acquire data, and new applications for existing data
- Work with other members of the data team, including data architects, data analysts, and data scientists
Skills needed to become a Data Engineer
Data engineers need to be comfortable with a wide array of technologies and programming languages. These are constantly subject to change, so one of the most important skills that a data engineer possesses is the underlying knowledge for when to employ which language and why. Data engineers must be interested in constantly updating their technical skill-sets. A good data engineer will possess knowledge of and skills in all of the following:
- Building and designing large-scale applications
- Database architecture and data warehousing
- Data modeling and mining
- Statistical modeling and regression analysis
- Distributed computing and splitting algorithms to yield predictive accuracy
- Proficiency in languages, especially R, SAS, Python, C/C++, Ruby Perl, Java, and MatLab
- Database solution languages, especially SQL, as well as Cassandra, and Bigtable
- Hadoop-based analytics, such as HBase, Hive, Pig, and MapReduce
- Operating systems, especially UNIX, Linux, and Solaris
- Machine learning, including AForge.NET and Scikit-learn
Clearly, data engineers are expected to have a wide array of technical expertise. Much of the job, though, requires critical thinking and the ability to solve problems creatively so that the right approach is used in the right situation. This might include creating solutions that don’t yet exist.
In addition, data engineers must also be able to work effectively in collaboration with other data experts, and communicate results and recommendations to colleagues without technical backgrounds.
Data Engineer salary
According to payscale.com, “A Data Engineer earns an average salary of $90,286 per year.” Experience has a positive effect on salary, with many data engineers staying in the field for 20 years or more. The highest-paid data engineers employ their skills in programs such as Scala, Apache Spark, Java, and in data modeling and warehousing.
Data Engineer job outlook
According to the tech firm Stitch, the number of data engineers in the country increased 122% from 2013 to 2015. In fact, Stitch reported a larger increase of data engineer jobs than of data scientist jobs. This is likely due to the fact that secure data infrastructure is necessary for any company looking to implement data mining techniques and later gain actionable insights.
Many of these new data engineers came from a background in software engineering, and brought to this field their skills in Linux, Java, SQL, Python, and Hadoop. As this career continues to grow and change, data engineers can gain leverage by staying at the forefront of advances in data management.