The history of data engineering dates to the dawn of SQL servers and ETL (extract, transform, load) procedures. Arguably, data engineering’s roots can be traced to the age of IBM database management systems in the 1970s. In the 1980s, the term “information engineering” was used to describe database design and software engineering.
Data engineering describes a type of software engineering that is focused on developing tools to manage data accurately and quickly. Big data and the growing influence of data-driven companies such as Facebook, Google, and LinkedIn have given rise to the modern concept of data engineering. The field of data engineering includes building data infrastructures, as well as the warehousing, mining, and modeling of data.
For those seeking information on how to become a data engineer, it is worth noting that it is a high-demand top-flight tech job. According to the hiring website Dice, job postings for data engineers over the 12-month period between 2018 and 2019 increased by 88.3%, surpassing roles such as computer scientist, data scientist, and database architect.
What Does a Data Engineer Do?
There are many types of data professionals, from data scientists to data analysts. The key to understanding the difference between data engineers and other data roles lies in understanding what an engineer does. For example, the primary responsibility of an engineer is to design and build things. Therefore, data engineers build the systems and software that store, process, and transfer data. These systems make the data usable for data scientists and analysts who are looking to glean insights from it.
In the hierarchy of data science, data engineers lay the groundwork for the success of data scientists. Data and information typically come in various forms, including unstructured forms that have missing or mismatched fields. The role of the data engineer is to use their programming, integration, and architecture skills to “clean” the data so data scientists can extract value from it. Without the data engineering phase, data scientists cannot analyze the data and build models that provide business or research value.
In the workplace, data scientists and data engineers often work together. For example, data scientists analyze data, but only after data engineers make it possible for them to use it by developing the infrastructure for data generation, preparation, storage, and analysis.
While data scientists work closely with the business side of an organization to develop a data strategy, data engineers support data scientists by ensuring the data is available and of high quality. Only by using the systems and pipelines that data engineers build can data scientists then generate business insights.
The role of the data engineer is vital to minimizing data silos, which isolate data and prevent its use by other parts of the organization. To help ensure easy access to usable data, data engineers use data science programming languages such as Java, Python, and R. They also apply their deep knowledge of databases and distributed systems to build integrated data infrastructures that enable data scientists and others to use the data.
Steps to Become a Data Engineer
The path to becoming a data engineer involves multiple steps, including skill development, education, and certification.
Skills critical for success in data engineering include advanced core competencies in programming. Languages typically used by data engineers include Java, Scala, and Python. Additional skills include knowledge of distributed systems and data pipelines, which is a term that describes bringing together multiple big data technologies that enable business processes.
By comparison, data scientists — the counterparts of data engineers — typically have skills in statistics, machine learning, artificial intelligence (AI), and advanced analytics. Many of the competencies required of data scientists are also helpful for data engineers, who often participate in projects involving advanced analytics and AI. In addition to honing their technical skills, data engineers can improve their opportunities for advancement by enhancing their analytical, interpersonal, leadership, and organizational abilities.
Individuals interested in pursuing a data engineering career can start with a bachelor’s degree. Areas of study include computer science, engineering, physics, mathematics, or a related subject.
A master’s degree in study areas including computer engineering and computer science can help individuals become more marketable. Because many of the necessary skills for data engineers overlap with those of data scientists, a master’s degree in business analytics can also help individuals advance in their careers.
A graduate degree in business analytics can serve as a bridge between data and strategy. For example, business analytics graduate degrees can provide data engineers with the skills to examine and interpret large data sets. Knowledge of business analytics also enables them to use advanced analytical tools and techniques and create unique data models and databases to support business solutions.
Professional certifications are not always required. However, earning certifications can accelerate upward mobility of individuals who are seeking leadership roles in the field. Common certifications include the following.
- Amazon Web Services (AWS)
- Certified Data Analytics – Specialty, Cloudera Certified Associate (CCA)
- Data Science Council of America (DASCA)
- Associate Big Data Engineer
- Google Professional Data Engineer
- IBM Certified Data Architect – Big Data
Data Engineer Salary and Job Outlook
Big data and technology impact every sector of the global economy. As a result, data engineers can work in a wide variety of industries, including aerospace and defense, business, finance, health care, higher education, hospitality, technology, and beyond. However, most data engineers work in the computer systems design field.
The U.S. Bureau of Labor Statistics (BLS) places data engineers in the category of computer network architects, which reports a median annual salary of $112,690 (as of May 2019). Salaries can range based on job location, education level, company, and experience. For example, a beginning senior-level data engineer with less than a year of experience can earn a median annual salary of $77,388, according to the website PayScale. The BLS projects 5% growth for data engineering roles between 2018 and 2028.
Leverage Your Desire to Solve Problems and Reinvent Your Skills to Advance Your Career
Are you looking to dig deeper into data and use it to help employers make critical business decisions? Ohio University’s Online Master of Business Analytics degree program equips students with the in-demand skills that help organizations leverage the power of big data and solve complex problems.
For individuals who are interested in pursuing careers as data engineers — begin today by exploring Ohio University’s Online Master of Business Analytics degree program.
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Student Interview: Online Master of Business Analytics
The Future of Business Analytics: Trends in Data and Business Intelligence
CIO, “Top 9 Data Engineer and Data Architect Certifications”
Dice, “Data Engineer Remains Top In-Demand Job”
Forbes, “The Changing Data Science And Data Engineering Tooling Environment”
O’Reilly, “Data Engineers Vs. Data Scientists”
O’Reilly, “Why A Data Scientist Is Not A Data Engineer”
PayScale, “Average Data Engineer Salary”
QuantHub, “What is Data Engineering and Why Is It So Important?”
QuantHub, “Will Demand for Data Engineers Fuel a Talent Shortage in 2020?”
Towards Data Science, “Data Engineer VS Data Scientist”
U.S. Bureau of Labor Statistics, Computer Network Architects