Big data engineers interact with massive data processing systems and databases in large-scale computing environments. They sort through the sweeping data to find relevant sets for analysis, which organizations then use to predict behavior.
Big data engineers provide organizations with analyses that help them assess their performance, identify market demographics, and predict upcoming changes and market trends. Almost every field needs big data engineers, as they produce insights that are useful in business, finance, government, healthcare, science, telecommunications, and other industries.
What Does a Big Data Engineer Do?
Before delving into what big data engineers do, it is important to understand what big data is. According to the U.S. Bureau of Labor Statistics (BLS), big data is the collection and analysis of information that organizations are generating at unprecedented scales. Much of the data comes from such sources as e-commerce, smartphones, and social media — all of which are relatively new technologies.
Big data as a research discipline is still evolving. As a result, classification and comprehensible understanding of the phenomenon remain elusive. Big data has the potential to predict market fluctuations, industry shifts, and other trends with unprecedented accuracy. Using big data means seeing beyond just a few immediate data points — it’s about taking in the bigger picture based on a much wider range of data.
This near-constant stream of data must be managed by someone who can interpret the information and produce actionable insights. This is the job of big data engineers — also known as data scientists, statisticians, and computer and information research scientists.
What a big data engineer does is complete many different tasks using skills drawn from many areas. For example, they may be responsible for the following tasks:
- Work with data architects and IT teams on formulating project goals
- Build highly scalable data management systems from the design phase to completion
- Design top-tier algorithms, predictive models, and prototypes
- Create data set processes to be used for data modeling, mining, and production
- Develop custom analytics apps and other kinds of software
- Ensure that data systems meet specific requirements
- Oversee disaster recovery preparations
- Research improvements to data quality, reliability, and efficiency
- Look for data acquisition opportunities as well as new uses for existing data and tools
Those interested in becoming a big data engineer can prepare by developing problem-solving skills and gaining database and data integration knowledge. Some of the most difficult tasks assigned to big data engineers pertain to sorting through chaotic, unorganized sets of data from many different sources and in as many different formats. Big data engineers aim to turn that messy information into clean, accurate, and actionable data — understandable to anyone receiving reports based on the information.
Steps to Become a Big Data Engineer
The professional path to become a big data engineer involves education, work experience, and optional certifications. Each step of the way, engineers can sharpen their skills and knowledge, potentially boosting their chances of getting hired.
Step 1: Education
The first step toward becoming a big data engineer is fostering an interest in computer science, math, physics, statistics, or computer engineering. These subjects are usually introduced in high school and expanded upon in undergraduate and postgraduate programs. Big data engineers hold at least a bachelor’s degree, with most also having an advanced degree, such as an online master’s in business data analytics.
The added years of study are crucial for learning the myriad technical skills that a big data engineer needs. The advantages of having a master’s degree include gaining advanced analytical and software engineering expertise in such areas as database principles, data visualization, business data analytics, data mining, and forecasting and predictive modeling.
Here are some of the technical areas in which professionals may need to be proficient to advance in this career:
- Database architectures
- SQL, including PostgreSQL and MySQL
- Data modeling tools such as Erwin and Enterprise Architect
- MatLab, SAS, and R statistical programs for machine learning
- Algorithms for predictive modeling, natural language processing (NLP), and text analysis
- Statistical modeling and analysis
- Business analytics and intelligence using cloud computing tools such as Microsoft PowerBI and Azure
- Hadoop’s MapReduce compiled language, Hive query language, and Apache Pig scripting language
- NoSQL databases, such as Cassandra and MongoDB
- Programming languages: Python, R programming, C/C++, Java, and Perl
- UNIX, MS Windows, Linux, and Solaris operating systems
Step 2: Work Experience
Gaining work experience, even while earning an advanced degree, can help students develop the capabilities a big data engineer needs to succeed: communication, problem-solving, analytical skills, critical thinking, logical thinking, and attention to detail.
IT professionals looking to grow into a big data engineer role must also hone additional skills outside of the classroom. These interpersonal and business skills include the ability to collaborate, a curiosity to continue learning, and an enthusiasm for finding creative solutions to complex challenges.
Step 3: Certification (Optional)
There is another step to consider before applying to big data engineering positions — certifications. Professionals may stand out from their competitors and become more appealing to employers by attaining certifications that demonstrate their proficiency in key skills. Some certifications require having an advanced degree, while others have no special prerequisites. Big data scientists may seek the following professional certifications:
- Cloudera Certified Professional (CCP) Data Engineer. Cloudera certifies professionals in the following skills: data analysis, workflow development, data ingestions, data staging and storage, and transformation. The certification exam takes four hours to complete and costs $400. There are no prerequisites required.
- Certified Big Data Professional (CBDP). The CBDP certification focuses on testing for proficiency in data science and data business intelligence. The Institute for Certification of Computing Professionals developed this certification, the cost of which varies based on the level of the test. Depending on the level of certification, candidates are required to have at least one year of technical experience and a BA degree.
- Google Cloud Certified Professional Data Engineer. The Google Cloud certification tests proficiency in building data structures, designing data systems and analyzing and designing for machine learning, reliability, security, and compliance. This certification exam takes two hours to complete and costs $200. There are no prerequisites required.
Big Data Engineer Salaries
The BLS doesn’t collect information on big data scientists. Instead, it cites similar jobs, such as statistician, mathematician, and computer and information research scientist. Here are just a few BLS figures from May 2017 that are representative of big data engineer salaries:
- Statisticians earn a median annual wage of $84,060.
- Computer and information research scientists earn a median annual wage of $114,520.
PayScale shares the following big data engineer pay points:
- Big data engineers report salaries in the range of $66,000 to $130,000, with an average annual salary of $89,838.
- Data scientist median annual salaries range from $63,000 to $129,000 and average $91,784.
These big data engineer salaries are largely dependent upon levels of education and experience: professionals holding master’s or doctoral degrees and/or possessing extensive experience earn more than their less-qualified counterparts. As professionals gain more knowledge and experience, their specialized skills will overlap, which makes their cross-applicability immensely attractive to prospective employers.
Employment Outlook for Big Data Engineers
As previously mentioned, the BLS places big data engineers under the categories of statisticians, computer programmers, and computer and information research scientists. Here are growth projections for these professions:
- The BLS predicts statistician positions will grow by 34 percent between 2016 and 2026, which is much faster than the projected 7 percent average growth for all occupations in the U.S. in that period. That translates to an added 12,600 new jobs available to qualified professionals. Statisticians represent the seventh-fastest-growing occupation in the U.S., according to the BLS.
- The BLS predicts computer and information research scientist jobs will grow by 19 percent between 2016 and 2026, with an added 5,400 jobs.
Additional career sites also note the rapid growth predicted in the big data engineer sector. For example, Glassdoor lists data scientist as the No. 1 best job in America for 2019, with an estimated 6,510 new openings and a job satisfaction rating of 4.3 out of 5.
Accelerate Your Big Data Scientist Career Potential
A career as a big data engineer may be the perfect fit for professionals eager to join an in-demand, fast-growing field that aligns with their love for computer science, math, statistics, or computer engineering. Start your journey toward becoming a big data engineer today by learning about Maryville University’s Master’s in Business Data Analytics Online.