As organizations strive to harness the immense potential of Big Data, the demand for skilled data engineers has soared. Data engineers must construct and maintain repositories for data, including massive databases. Without them, data scientists and data analysts wouldn’t be able to do their jobs effectively.
Data engineering is a complicated job. With the growing reliance on data-driven decision-making and as companies collect vast amounts of data, there is an urgent need to design, build, and maintain robust data infrastructure systems. But does it require a degree? Let’s dig in!
What do data engineers do?
Data engineers play a pivotal role in managing and processing this information, ensuring its quality, security, and accessibility. Jon Osborn, currently field CTO of Ascend.io, recently told Dice that, if he were a data engineer starting out right now, he would get a basic cloud certification (AWS/GCP/Azure), learn SQL and Python, and seek at least a basic certification in popular data platforms such as Databricks, Snowflake and/or BigQuery.
At the outset of a data engineering career, it’s all about technical skills. On top of learning key data engineering theory and tools, you must also grasp the needs of your particular industry. For example, healthcare companies need data engineers who understand the nuances of privacy and security around patient data. Data engineers also need solid people skills, as they’ll be tasked with conveying challenges and results to a wide array of stakeholders, from executives to data analysts.
As data engineers mature into their career, they’ll be expected to adopt new leadership skills, especially as they’ll often end up leading teams. They’ll also need to continue updating their knowledge in everything from cloud platforms to data analytics engines. Here’s a more granular breakdown of the data engineer skills you’ll need at every stage of your career.
Are degrees important for data engineers?
By obtaining a data engineering degree, individuals position themselves as experts in this pivotal field, making them highly sought-after assets in today's job market. The skills acquired through a data engineering degree include database management, programming languages, data integration, and advanced analytics.
These programs typically cover a range of essential topics such as database management, data warehousing, cloud computing, programming languages (e.g., Python, SQL), data modeling, data integration, and data quality assurance. Students also delve into advanced subjects like distributed systems, machine learning, and data analytics, equipping them with the knowledge and skills necessary to tackle complex data engineering challenges.
One notable advantage of pursuing a data engineering degree is the hands-on experience gained through practical projects and internships. Many programs emphasize real-world applications, allowing students to work with industry-standard tools and technologies.
By engaging in these experiential learning opportunities, aspiring data engineers acquire a solid foundation in data manipulation, ETL (Extract, Transform, Load) processes, data pipeline construction, and data governance.
Kenneth Sardoni, senior VP for learning programs at CompTIA, points to top-level research institutions including Oxford, MIT, Stanford, Brown, Berkeley and Carnegie Mellon as institutions offering excellent programs for data engineering degrees at the graduate and PhD level. “But we're seeing it in the regional and state schools all the way through as well,” he says. “There's a regional university here in Utah, Utah Valley University, and they've started a data engineering program and the data analytics program as well.”
How should I select a data engineering program and/or degree?
Sardoni says there are a couple of criteria that aspiring data engineers should take into account when selecting a data engineering degree, chief among them being the tools and the skills that are being taught in the program and how hands-on those tools are.
“I would look at things like programing languages—Python would definitely be a must, along with Java and Scala,” he says. “I would make sure that they had Big Data technologies, that they covered things like Hadoop and Spark. I would want to make sure that they had data warehousing technologies like Redshift and Snowflake.”
Any good data engineering degree program will include ETL tools: for example, Talend and Informatica, along with the cloud platforms. “You'd have to cover AWS, Azure, and Google's offering, and make sure that they cover it not just from a theoretical standpoint, but hands on as well,” Sardoni says.
The institution must also teach an engineering approach. “When Big Data came out, huge data lakes were built which were used to store a lot of data. However, little thought or data engineering was designed into these data lakes for efficient and effective data retrieval,” he says. “This made it very difficult to gain benefit from the data. I would want to make sure that the degree program really focuses on the data engineering lifecycle, as well.”
That means ensuring the institution will teach data engineers how to bring data together from disparate sources and transform the aggregation into a useable form. “Make sure the program offered topics like security as well, which is a huge element of data engineering,” he says. “We must ensure data is secure so they would have to teach management of data and architecting these systems, as well as data governance.”
What else should data engineers consider when pursuing a degree?
As Coursera chief content officer Marni Baker Stein points out, it is crucial to research and choose a program that aligns with your career goals, offers a comprehensive curriculum, and provides opportunities for practical application.
In addition to the technical skills, all data engineers should have solid soft skills such as empathy and communication. “It's important to be a good team member or a good leader, so make sure they're being holistic in the way they develop the data engineer,” she explains. “This is important because a data engineer operates inside of a broad work environment where they have to have lots of skills beyond database engineering, data science and data analytics.”
Before selecting a degree program, it’s a good idea to explore your potential commitment to data engineering in advance through a low-risk, low-cost avenue, rather than taking that leap into a program you don’t end up loving. For example, many online learning portals have free or low-cost courses that allow you to explore intro to data engineering, including Coursera and Udemy.
If possible, it’s also great to talk to data engineers about their experiences, challenges, and love for the job. Forums such as the r/dataengineering forum on Reddit can introduce you to people who will give advice.
“When you're going into any pathway, really understanding the job roles and the institutional context that those jobs function in is really important,” Baker Stein says, adding that data science is being disrupted like every other field, thanks in large part to evolving technologies such as generative A.I. and a new generation of data analytics platforms.
“You need to think about whether or not those are of that's an experience or an everyday reality that you are ready to embrace and that you would enjoy,” she adds.
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