Data engineers design, manage and optimize the flow of data within an organization. And in an age of big data and AI, that’s one of the most important and in-demand jobs. According to DICE’s 2020 Tech Job Report, data engineer was the fastest-growing job in 2019, growing by 50 percent. This report also stated that filling data engineering positions takes approximately 46 days. The need has only grown since then, with data engineers among various industries’ most critical roles.
For example, when a medical facility first transitions to electronic health records and digital collection, it’s awash with data and most of that data ends up in isolated silos. But data only produces searchable, actionable insights when used in conjunction with other data. That’s where a data engineer comes in, building an infrastructure of data pipelines, distributed systems, and a singular data lake from which all data can be securely deposited and queried. Operationalizing an institution’s data resources like that has a high, quantifiable value, which is part of the reason why data engineers are paid so handsomely, with most earning well over $100,000 per year.
The BLS (May 2021) does not have any information on data engineer salaries, but it notes the median salary for database administrators and architects was $96,710 in May 2021. The BLS also has salary information for computer network architects, a field that is closely related to data engineering, stating that the median pay for computer network architects was $120,520 in May 2021. PayScale (March 2023) reports that the average salary for data engineers is $94,253.
While there is frequent collaboration between data scientists and data engineers, they’re different positions that prioritize different skill sets. Data scientists focus on advanced statistics and mathematical analysis of the data that’s generated and stored, all in the interest of identifying trends and solving business needs or industry questions. But they can’t do their job without a team of data engineers with advanced programming skills (Java, Scala, Python) and an understanding of distributed systems and data pipelines. Some companies and universities still merge the roles of data scientist and data engineer, but this is trending down and the need to separate these roles is increasingly important.
Compared to careers in law and medicine, the role of a data engineer is still so young that there aren’t many clearly defined steps to becoming one. A multitude of paths exist. The critical badge for any data engineer is not necessarily an advanced degree, but a true demonstration of capability. How one develops and certifies that capability is a customized and personalized journey.
Check out our step-by-step guide below, and start engineering your future.
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After graduating from high school, aspiring data engineers must earn a bachelor’s degree, ideally in computer science. Admissions requirements will vary from school to school, but typically include a competitive GPA (3.0 or greater), SAT or ACT scores, and a personal statement or letters of recommendation. Previous STEM experience can be seen as a bonus. Once enrolled in an undergraduate program, any opportunities for hands-on experience should be sought out and undertaken, as data engineering is much more practice-based than theory-based.
The University of Florida offers an online bachelor’s degree in computer science through the College of Liberal Arts and Sciences. This online program offers maximum flexibility for students with other commitments who cannot attend campus. The curriculum of this program is taught by the same elite faculty members who teach on campus.
Combining computer science with a liberal arts education, the program includes required foundational coursework (which may be transferred to another institution) in analytic geometry and calculus; computational linear algebra; physics with calculus; and engineering statistics. Core coursework includes classes in programming fundamentals; information and database systems; data structures and algorithms; and digital logic. The program consists of 120 credits.
At the end of the program, graduates can pursue opportunities such as database administrators, computer programmers, business intelligence analysts, computer systems analysts, network systems administrators, software applications developers, and web developers, among many such roles.
Regis University also offers an online bachelor’s degree in computer science, helping students develop the required knowledge and skills in programming, algorithms, data structures, systems security, database applications, and more. Students will graduate with a strong grasp of the foundations of computer science and will develop an intuitive understanding of the challenges.
In addition to breadth requirements, students take courses in data structures, algorithms, and the principles of programming languages. Upper-division courses include data science, database management, distributed systems, and artificial intelligence. The program consists of 120 credits.
To apply to this program, applicants must submit a completed online application form, official transcripts from all colleges or universities attended, a current resume, and an admissions essay.
Regis University also allows students to accelerate their education even more by earning their bachelor’s and master’s degrees simultaneously through the FastForward program.
Upon completing the program, graduates can take up roles such as software engineers, web developers, application developers, data scientists, network architects or engineers, and systems analysts.
Data engineering—like many computer science fields—tends to lean towards meritocracy. If you’re the most capable candidate, then you have a good chance of being hired. It’s entirely possible to be hired for an entry-level job out of college, and that’s a perfect opportunity to start building a portfolio of experience and achievement in the field. Work experience is its own education, and a little work goes a long way in helping to assess one’s level of competency and determine their next steps.
While it’s not necessary, earning a master’s degree in computer science can be useful for those who want to leave their options open for crossover roles between data engineering, data science, and management. In addition to learning advanced skills, students of graduate programs can also build their professional networks and get career mentoring as a result of their enrollment. Admissions requirements vary from program to program, but often include some combination of the following: a competitive GPA (3.0 or greater), GMAT or GRE scores, letters of recommendation, a personal statement, and some level of work experience.
Arizona State University has a master of computer science (MCS) program offered through the Coursera learning platform that can be completed entirely online. Ideal for students with an undergraduate degree in computing or a related discipline, this online program provides students with a deep understanding of advanced topics such as cybersecurity, big data, AI, blockchain, and software engineering, while strengthening their skill set through real-world projects. The program also allows students to choose from two available concentrations: Cybersecurity and Big Data Systems.
Classes cover topics such as the foundations of algorithms; information assurance and security; data processing at scale; knowledge representation and reasoning; mobile computing; distributed and multiprocessor operating systems; applied cryptography; and data visualization. The program consists of 30 credits.
Graduates of the program can pursue roles such as computer network analysts, computer programmers, computer scientists, computer systems analysts, database administrators, information technology managers, SEO specialists, and software developers.
Colorado State University offers an online master of computer science program. Taught by experienced and dedicated faculty members, the program helps students gain in-depth knowledge in areas such as parallel computing, systems software, software engineering, database systems, and more.
Applicants to the program must have a bachelor’s degree from a regionally accredited institution with a grade point average of 3.0 on all undergraduate coursework and a grade point average of 3.2 in computer science and mathematics. Application requirements include three letters of recommendation, a current resume, a statement of purpose, unofficial transcripts, and TOEFL or IELTS scores for international applicants. GRE test scores are not required for admission.
The program consists of 35 credits, including coursework in an introduction to computer graphics; introduction to artificial intelligence; object-oriented design; introduction to machine learning; database management systems; and parallel programming.
On successful completion, graduates will be ready to work in some of the top aerospace, computer software, and high-tech companies.
Those interested in performing crossover duties between data science and data engineering may choose to pursue an online master of computer science in data science offered by the University of Illinois. Students in this program will be provided with graduate-level expertise in four core areas of computer science: machine learning, data visualization, cloud computing, and data mining.
The major admission requirements include a four-year bachelor’s degree equivalent to that granted by the University of Illinois, a minimum grade point average of 3.0, a completed online application, unofficial transcripts, three letters of recommendation, a statement of purpose, a current resume, and English language proficiency for applicants whose native language is not English. GRE scores are not required for admission.
Breadth courses cover topics like applied machine learning; database systems; data visualization; and cloud networking. Advanced coursework adds on classes in advanced bayesian modeling; the foundations of data curation; and the practice of data cleaning. The program consists of 32 credits.
Those looking for short-duration, targeted education on data engineering can turn to short-term engineering courses. While not required, they provide hands-on experience and can culminate in a professional certificate. In a way, they’re a sort of hack: they do away with the bloat and offer advanced training at a fraction of the cost and time a more general advanced degree would.
Coursera hosts a series of short courses that make up a specialization in data engineering on the Google Cloud Platform. Designed and taught by Google teams, there are five courses in the specialization: Google Cloud Platform big data and machine learning fundamentals; modernizing data lakes and data warehouses with GCP; building batch data pipelines on GCP; building resilient streaming analytics systems on GCP; and smart analytics, machine learning, and AI on GCP.
This intermediate-level program takes approximately four months, with four hours of study per week. While this specialization doesn’t equate to Google certification (see step five below), it gives students solid foundational knowledge which, combined with work experience, can aid one’s pursuit of official certification later on.
Coursera also hosts a series of short courses in data engineering that make up its data engineering foundations specialization. Offered in partnership with IBM—a global leader in business transformation through an open hybrid cloud platform and AI—this specialization helps anyone interested in pursuing a career in data engineering by teaching them the fundamental skills needed to get started in this field. The courses cover the following subjects: introduction to data engineering; python for data science, AI & development; python project for data engineering; introduction to relational databases (RDBMS); and databases and SQL for data science with python. The specialization takes approximately five months to complete, with five hours of study per week.
In a young and dynamic discipline like data engineering, professional certification offers perhaps the most concrete way to verify one’s skills and capabilities. Built by and for working data engineers, these certifications measure anyone by standards agreed upon within the dynamic data engineering community. And while academic institutions are notoriously slow-moving, today’s tech giants are surprisingly nimble, and certifications from industry players can hold great significance to employers in proving a prospective employee’s talent.
One such certification is the Google Cloud Certified Professional Data Engineer, which has no prerequisites for eligibility. Earning this certification simply requires passing a two-hour, in-person, multiple-choice exam. The exam is divided into four sections: designing data processing systems; building and operationalizing data processing systems; operationalizing machine learning models; and ensuring solution quality. Google offers both instructor-led and on-demand training for the exam. Certification is valid for two years, after which applicants must recertify. The registration fee is $200.
Those who wish to pursue an internationally-recognized, company-agnostic certification can look to the Data Science Council of America (DASCA). The DASCA offers certification as an Associate Big Data Engineer (ABDE) and a Senior Big Data Engineer (SBDE). To apply for the ABDE, one needs only a bachelor’s degree in computer science or a related field. An applicant for the SBDE needs either a bachelor’s degree and two years of work experience or a master’s degree and one year of work experience.
To become certified, applicants for either certification will need to pass an exam based on the DASCA Essential Knowledge Framework. Both exams cover the following areas: foundational data science; big data analytics basics; data processing framework & Hadoop; R and Hadoop applications; streaming data storage; analytics in machine learning and AI; streaming data architectures; enterprise data analytics implementation; and streaming & batch data processing. Study materials are available on the DASCA website. The registration fee is $585 for the ABDE and $650 for the SBDE.
Data engineers need to be resourceful sleuths who grab insights and tools from wherever they can. As always, the data is out there and needs to be wrangled. If you want to get an idea of what’s available and what’s being talked about in data engineering today, check out some of the following resources:
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