How to Become a Data Engineer
What is a Data Engineer?
With the rising dependence of corporations, governments and agencies on large sets of data, there has also been a growing demand for professionals who can handle the deployment, maintenance and use of supporting systems. This includes dealing with work like setting up servers, computing clusters, networks and databases to ensure optimal processing speed and power when data scientists and other stakeholders need to information. As the Big Data field has grown, the job of the data engineer has become critical.
At its core, the profession of a data engineer is about making sure the backbone of Big Data systems is strong. They need skills in dealing with server hardware and configurations, analytics software packages and troubleshooting complex systems. A data engineer may be asked to address everything from overheating problems on machines to questions about how to format information for input into analytics systems. Before massive data insights are turned into actionable information for stakeholders, an organization’s data engineers need to make sure everything is running smoothly.
Career Outlook
With the explosion in Big Data startups and an influx of major companies playing catch-up, coming up with precise figures for job demand for data engineers is a challenge. The job is so cutting-edge that the Bureau of Labor Statistics doesn’t even have a classification for it yet, instead lumping it in with older occupations like database administrator. According to a report from Glassdoor, a website that tracks employment trends, Data Engineer is listed as the #8 best job in America in 2019 and rated as ‘The Best Job of 2020’ from Harnham.
Glassdoor says there 4,739 openings for data engineers. According to a report by KDnuggets, a website that follows Big Data, data science and machine learning news, annual growth in data science fields, in general, is about 16%. One big upside to moving into data engineering is that there is also an obvious career path into data science.
Salary
Data engineers can expect to earn median salaries between $100,000 and $110,000 per year. Notably, as the field has expanded, median salaries have dropped a bit. This is likely due to a degree of normalization in the profession as the market figures out exactly what the demand is.
It’s worth looking at trades with comparable skills. Data scientists clock in at median salaries of more than $128,000 per year. Financial advisors, whose jobs are become increasingly data-driven, make about $89,000 per year. Software engineers earn around $100,000 per year. In other words, data engineering holds up well in terms of salaries given as compared to similar occupations.
How to Become a Data Engineer
The range of skills required of data engineers is pretty big, and you’ll eventually have to gravitate toward a specific software stack as your specialty. Data engineers need to be trained in using a number of database languages, and that entails being familiar with NoSQL, MySQL, MSSQL and Mongo DB.
They also should have a solid understanding of computer programming languages. Good ones to learn include:
- Python
- Java
- R
- Perl
- SAS
Most folks who move into data engineering have some sort of academic background in IT, computer science or statistics. If you’re not comfortable with math and code, it’s not a career path you’ll want to jump right on without brushing up. Fortunately, you’ll be expected to have at least a master’s degree before you move on to a Master of Science in Data Analytics or an MS in Data Science.
Education and Coursework
Most programs have specialist tracks for folks who aim to become data engineers. You can expect to handle coursework that covers problems like:
- Data collection
- Choosing the right hardware and software for a project
- Building new software for specific applications
- Security, legal and ethics concerns related to data gathering and mining
- Thinking about data at a system-wide scale
- Creating workflows that optimize machine learning performance
- Scaling projects up using systems like MapReduce and Hadoop
- Working with teams to design systems and achieve goals
When you’re done with your coursework, you should have completed several large projects that can be presented to employers as evidence of your talents and interests. Likewise, working through projects will help you develop a feel for which software stacks work best for you and what kinds of jobs you find appealing. In many cases, students pursuing a master’s degree will have the opportunity to work directly with professionals from companies, organizations and government agencies that are developing state-of-the-art Big Data systems.
A Day in the Life
A data engineer often has a set of tasks they have to get done first thing in the morning. For example, one working for an online review website might have to ingest logs from users for the last 24 hours. The data has prepared for multiple use cases, and it has to be prepared in a manner that allows folks working upstream, such as data scientists and market analysts, to grab the data and get started on their own work.
A data engineer may then have to check in on real-applications that their departments are running. In many cases, their goal is to be the human that’s in the loop to ensure that machines avoid errors. They will also be the first folks to issue alerts when problems have been identified.
If there are new projects in the pipeline, an organization will task a data engineer with developing specs for it. This may include looking at established and emerging technologies to determine what represents the best match. Once a set of specs is ready to go, a data engineer will then ship a proposal for the project.
Certifications and Continuing Education
Certification is very hardware- and software-specific in the data science field. Depending on the architecture a company utilizes, you may need to be certified in using Google Cloud, Amazon AWS, Cloudera or Microsoft Solutions. Many companies will help promising recruits get the necessary certifications for their new jobs, though. The big thing is to use your coursework and personal projects to demonstrate your interests and show your general competency.
Sources:
https://sps.northwestern.edu/masters/data-science/curriculum.php
https://www.niu.edu/online/graduate-programs/ms-data-analytics.shtml
https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm
https://www.kdnuggets.com/2017/01/glassdoor-data-scientist-best-job-america.html
https://blog.insightdatascience.com/a-day-in-the-life-of-a-data-engineer-35efacaa6b2e