Data Engineer: How to Become One?

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The business world is flooded with large amounts of data that must be processed and analyzed in order to assist decision-makers in areas including marketing, sales, production, distribution, and staffing. To manage and optimize this data flow, these businesses rely on data engineers.

As a result, data engineers are in high demand.

Education for Data engineering

A data engineer typically requires at least a bachelor’s degree. The bachelor of science in data science, the bachelor of science in data analytics, or the bachelor of science in computer science are among the four-year degree programs you might consider. Over 100 US colleges and universities offer degrees in data science.

To advance in this field, typically a master’s degree is required. Many companies prefer applicants with a master’s degree, even for non-management positions. You can earn a master of science degree in data science, a master of science degree in analytics, or a master of science in analytics. If you are considering an MBA with a concentration in data analytics, you could also consider a master of science in information systems with a focus on database management. 

A master’s degree program is likely to cover more advanced topics in predictive analysis, data trends, decision support, statistical analysis, machine learning theory, data architecture, and forecasting. Data science internships are also available for graduates. Some large companies, provide internship opportunities wherein you can learn about data retrieval, forecasting, statistical modeling, and systems development. 

Becoming a Data engineer

Data engineering boot camps provide a quick and efficient way to learn various aspects of the field. You can learn data mining, architecture, programming, warehousing, and other skills through hands-on, project-based methods. Boot camps effectively expand your knowledge, improve your skills, and brush up on advanced concepts to enhance your chances of landing a job. Upon completion of a boot camp, you might be hired and pursue your degree while working. 

A certification demonstrates your skills and depth of knowledge in a wide range of areas, including programming, analytics, data systems design, and more. It reinforces your expertise in specific industries. Certifications are typically offered by technology companies and professional associations. For instance, Google alone offers eight certifications in and related to data engineering, such as Cloud Network Engineer, Machine Learning Engineer, Data Engineer, Cloud DevOps Engineer, and Collaboration Engineer. 

Other certifications in data engineering include:

  • Amazon: AWS Certified Data Analytics – Specialized;
  • SAS: Big Data Certified Professionals;
  • Cloudtera: Generalist for Cloudera Data Platform;
  • Microsoft: Associate, Azure Data Engineer;
  • Databricks: Professional Certified Data Engineer.

Job duties of Data engineers

Data engineers are primarily responsible for developing and implementing systems to assist companies in transforming raw data into usable information that can be processed and analyzed. By doing so, managers can make decisions and develop solutions. Using their knowledge of coding and programming, they develop databases, servers, processing systems, and data warehouses.

Typical duties of a data engineer include optimizing data delivery systems, analyzing internal data processes, designing analytical tools, maintaining pipeline systems, and creating complex data sets.

An engineer for data would have the following responsibilities:

  • Participate in the design, development, testing, and maintenance of architectures;
  • Incorporate business requirements into architecture;
  • Data collection;
  • Establish data collection processes;
  • Understand and use programming languages and tools;
  • Identify methods for improving the reliability, efficiency, and quality of data;
  • Conducting research for relevant business questions;
  • deploying ML, analytics programs, and statistical methods; 
  • discovering hidden patterns; 
  • automating tasks using data.