How to become a Data Engineer – A Complete Career Guide
Data science is the fastest-growing industry in the world right now, and data engineers are right there at the front of the pack. With perhaps the best job outlook of all data science roles (and that is saying something), pursuing a data engineering role is absolutely one to consider and Discover Data Science is here to let you know what it takes to make that role yours.
Data engineers are necessary in the big data revolution to build, test, and maintain data architecture. They are closely linked with data architects—in fact, these two positions must collaborate on most projects. You can consider the relationship similar to that of a real-world architect and engineer, an architect can design a beautiful building, but it’ll take an engineer to actually build it. 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.
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 engineers are also responsible for monitoring the movement and status of data in the systems that they develop, which can involve categorizing and cleaning large datasets when they become available.
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 Data Management Association (DAMA) International, is the Certified Data Management Professional (CDMP) credential. Those who reach a certain threshold on the examination can achieve this certification at various levels: “associate” (6 months to 5 years experience), “practitioner” (2 to 10 years experience), “mastery” (over 10 years experience), and “fellowship” (over 25 years experience.) 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.
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
As of January 2023, the Bureau of Labor Statistics (BLS) doesn’t list an average salary for data engineers specifically. However, they report that data architects (who often require similar experience and education to their engineer counterparts) earn a median annual salary of $96,710.
In certain cities, a data engineer’s earning potential may be even higher. In the tech hub of San Francisco, the annual mean wage for data architects and related roles is $161,830 according to BLS data. San Jose, California, hosts the highest annual mean wage for this role at $187,070.
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. The BLS reports that the top 10% of professionals in this role often make upwards of $169,500 annually.
Data Engineer Job Outlook
One of the greatest aspects of the data engineer career path is how amazing the current job outlook for the role is. According the 2020 Dice Tech Jobs Report, data engineer was actually the fastest growing tech occupation in 2019, with a growth of 50% over the previous year, surpassing all other roles for data scientists and developers. In May 2021, the BLS reported a more modest employment growth rate at 9% through 2031, but that translates to roughly 11,500 new job openings every year.
This sizable growth 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 the new data engineers in the industry 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.
Gain the skills and necessary degree to pursue your career as a data engineer. Explore the difference between a Data Scientist and a Data Engineer or data science certifications, including infrastructure and data engineering, and take the next step in your journey. Your future as a data engineer awaits you!
2021 US Bureau of Labor Statistics salary and employment figures for database architects reflect national data, not school-specific information. Conditions in your area may vary. Data accessed January 2023.
