This guide is all about how to become a data engineer. It includes information on what kind of degree or education is best-suited for getting started as a data engineer and potential career paths and outcomes.
Businesses worldwide are inundated with large amounts of data that must be processed and analyzed to help decision-makers provide solutions in operations, such as marketing, sales, production, distribution, and staffing. These businesses rely on data engineers to design and maintain systems to manage and optimize this data flow.
This translates into a high demand for data engineers. In 2019, data engineering was the fastest-growing job, according to the 2020 Dice Tech Job Report. Growth stood at 50 percent at the time of that report. Since 2016, the report further states that data engineers’ demand has far outgrown supply. In 2019, data engineer Carlin Eng wrote about job searching in this field and found that many companies had “aggressive hiring goals” for data engineers. When it came to the biggest challenge for these companies, “hiring was number one on the list.”
Data Engineer Degree
To begin a career as a data engineer, you would typically need to earn at least a bachelor’s degree. Four-year degree programs you might consider include the bachelor of science in data science, bachelor of science in data analytics, or a bachelor of science in computer science. More than 100 US colleges and universities offer degree programs in data science.
Common courses found in a bachelor’s degree program in data science or data analytics are:
- Big data
- Data mining
- Data modeling
- Applied statistics
- Data warehousing
- Business analytics
- Data visualization
- Database systems
- Database management
In these courses, you could learn about real-time analytics, mining software, machine learning applications, business intelligence, database design, data security practices, programming languages, data patterns, data structure, file management, data manipulation, and network modeling. Some bachelor’s degree programs contain internship opportunities so you can apply concepts studied in the curriculum in real-world situations.
Some companies might prefer a master’s degree, even for non-managerial positions. A master’s degree would also typically be required for advancement in the field. Master’s degrees you could pursue include master of science in data science, master of science in data analytics, and master of science in analytics. You could also consider a master of science in information systems with a concentration in database management or a master of business administration (MBA) with a concentration in data analytics.
At the master’s level, courses tend to focus on more advanced topics in predictive analysis, data trends, decision support, statistical analysis, machine learning theory, data architecture, and forecasting. Graduate internships in data science or data analytics are also available. Large companies such as GEICO and Gap, Inc. offer internships wherein you will experience hands-on learning opportunities in data retrieval, forecasting, statistical modeling, and systems development. Other companies such as Amazon, IBM, Capital One, and PayPal have hired MS in data analytics students and graduates for internships and full-time jobs.
Case study examinations and data analytics projects are generally major parts of master’s degree studies in this field, providing greater opportunities for hands-on learning and real-world exposure. Other practices that support a master’s degree program curriculum in data analytics, data science, or a similar area include conferences, symposia, online and live presentations, and career fairs. Hence, you have the chance to network and interact with professionals, faculty, and peers.
How to Become a Data Engineer
With the need to know about complex programming languages and coding, data transformation processes, technical design, and data processing and manipulation, few “entry-level” data engineer jobs are offered. Mainly, at least a bachelor’s degree is required to enter this field, as formal degree programs essentially cover many of the basics needed to begin in the field.
Bootcamps offer an accelerated way to learn various aspects of data engineering. These provide hands-on, project-focused learning methods for data mining, architecture, programming, warehousing, etc. These bootcamps effectively boost your knowledge, expand your skills, and brush up on advanced concepts to help demonstrate your abilities to prospective employers. After completing a bootcamp, you might obtain a position and then pursue your degree while working. Or, showing a hiring manager that you attended a bootcamp could show not only initiative but your interest in and dedication to the field.
Certifications are designed to display your abilities and depth of knowledge in programming, analytics, data systems design, and many other areas. These reinforce your skillset within industry-specific applications and systems. Technological companies and professional associations generally offer certifications. Google alone, for instance, offers eight certifications in and relating to data engineering, including Cloud Network Engineer, Machine Learning Engineer, Data Engineer, Cloud DevOps Engineer, and Collaboration Engineer.
Examples of other data engineering certifications follow:
- Amazon: AWS Certified Data Analytics – Specialty
- Data Science Council of America: Associate Big Data Engineer (ABDE)
- Data Science Council of America: Senior Big Data Engineer (SBDE)
- SAS: Certified Big Data Professional
- Cloudera: Cloudera Data Platform (CDP) Generalist
- Microsoft: Azure Data Engineer Associate
- Databricks: Certified Professional Data Engineer
What Does a Data Engineer Do?
The primary responsibility of a data engineer is to develop and use systems to help companies transform raw data into accessible information that can be analyzed and processed. This allows those in management positions to make decisions and create solutions. They apply their knowledge of programming and coding to develop databases, servers, processing systems, and data warehouses.
The duties of a data engineer would typically include optimizing data delivery systems, analyzing internal data processes, designing data analytics tools, maintaining data pipeline systems, and creating complex data sets.
According to the IT magazine CIO, the responsibilities of a data engineer would look like this:
- Develop, construct, test, and maintain architectures
- Align architecture with business requirements
- Data acquisition
- Develop data set processes
- Use programming language and tools
- Identify ways to improve data reliability, efficiency, and quality
- Conduct research for industry and business questions
- Use large data sets to address business issues
- Deploy sophisticated analytics programs, machine learning, and statistical methods
- Prepare data for predictive and prescriptive modeling
- Find hidden patterns using data
- Use data to discover tasks that can be automated
- Deliver updates to stakeholders based on analytics
Data Engineer Career Paths
An actual data engineer job description might look like this, based on an actual posting:
Senior Data Engineer
The data engineer will effectively extract, transform, load, and visualize critical data. They will build and ensure the accuracy of data pipelines driving faster analytics through data. This individual will work in an agile environment partnering with business, software application teams, and data scientists to understand their data requirements and ensure all the teams have reliable data that drives effective business analytics. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and enjoy working with a large scale of data.
- Build data solutions from the design phase to completion and ensure they meet specific requirements
- Build data pipelines, engineer complex new data sets, assess data quality, perform data engineering or ETL for data marts, visualizations, or data science models
- Query large data sets for ad hoc exploration, analysis, or testing
- Build a deep understanding of Spark (Databricks) and Python (Pyspark) to support your technical design solutions
- Build a deep understanding of the Azure Cloud Platform and stay updated on new capabilities, positioning yourself as a subject matter expert
- Build a deep understanding of Azure Data Factory, Databricks, ADLS, and Synapse (SQL Data Warehouse), so you can identify and recommend improvements to designs and strategies across the Azure technology stack.
- Support Agile Scrum teams with planning and scoping technical, analytic solutions, including time estimates for development and testing
- Participate in Agile scrum teams delivering data ingestion, validation, engineering, modeling, visualization, and analytics solutions.
- Engage with Technical Architects and technical staff to determine the most appropriate technical strategy and designs to meet business needs
- Liaise with data architecture, data engineers, and other technical contracting resources to work through technical dependencies, issues, and risks.
- Engage with business stakeholders to understand required capabilities, integrating business knowledge with technical solutions
- Communicate complex technical information to business customers and project teams in an effective and concise manner
- Adheres to applications security procedures, change control guidelines and coding structures, Sarbanes-Oxley IT, and business requirements
- Performs other duties as assigned
- Complies with all policies and standards
Other Example Career Paths
Skills typically highlighted for data engineers include organizational, analytical, communication, time management, problem-solving, and critical thinking.
After obtaining a degree in data science or a related area, you could choose from a few other career paths in addition to data engineering. You might focus on data infrastructures as a data architect or oversee creation and maintenance as a database administrator. A few other career options to consider are:
- Data scientist
- Data manager
- Big data engineer
- Machine learning engineer
- Business intelligence developer
According to Payscale.com, the average annual salary for a data engineer is just over $93,000. Senior data engineers realize a yearly salary of nearly $125,000. New York, Seattle, and San Francisco are among the top cities for data engineer salaries, Payscale further reports.