Data engineering involves building storage solutions for massive amounts of data and then finding ways to transform this data into useful information. Practically every industry relies on data engineers, and this reliance is only growing as the need for data-driven decision-making becomes more necessary.
But what actually is data engineering? In this guide, we’ll go over:
- Data Engineering Definition
- Data Engineering Industries
- Advantages and Disadvantages of Data Engineering Careers
- How to Get Into Data Engineering
- Skills Required for Data Engineering
Data Engineering Definition
Data engineering focuses on creating systems that collect data and make it usable for analysis. These engineers build systems, like data warehouses, databases, and data lakes, to store and transform data. Companies and organizations collect data from any number of sources, but the most important data typically comes from users and customers. This often includes users’ personal information, which has protection requirements from legal regulations. So, making secure and stable data warehouses is a top priority in the data engineering space.
But making this data usable is also essential. Raw data is often very messy, and it’s challenging to glean any valuable information from it when there are massive amounts of unorganized data.
Imagine an Excel spreadsheet with hundreds of thousands of rows, each with hundreds of columns — this is how a company’s user data could be gathered. Now, imagine that each row is a user, and each column includes specific details about that customer, such as recent purchases, contact information, and visits to the company’s site. Except, all the information in the spreadsheet is coded or in a sort of shorthand, so reading it at a glance is very difficult. Someone needs to take this data, put it somewhere safe, and then transform it into something that data scientists and analysts can use to make data-driven decisions — this is where data engineers come in.
Data engineers are becoming increasingly important “because good analysis can be done only on great data, and that data is only growing every day,” notes Dushyant Sengar, director of data science at BDO USA.
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Data Engineering Industries
One of the key industries that uses data constantly (and thus, needs data engineering to handle it all) is tech. Big tech companies like Facebook, Amazon, Apple, Microsoft, and Google take in massive amounts of data every day and using this data is part of how these companies make money. For example, Amazon uses your shopping data to influence what advertisements you see on Facebook. So, the data these companies get needs to be captured, stored, and transformed quickly and efficiently.
However, data collection and storage is a core aspect of almost every industry. Take health care, for example. Kaiser Permanente may use data from patients and details from research projects to improve diagnosis from CT scans or predict potential illness outbreaks.
On the other hand, a consulting firm like Deloitte may use data to inform how it recommends clients restructure their businesses or adjust marketing strategies.
In each of these examples, data engineering is needed to collect, store, and transform the data to allow these companies to make data-driven decisions.
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Advantages and Disadvantages of Data Engineering Careers
One of the biggest benefits of working in data engineering is that it’s an in-demand job: big data is only getting bigger, and the need for qualified data engineers to build storage systems will grow with it. However, inaccuracies in data engineering can cause massive problems down the line.
“Data accuracy and governance are needed since data growth is exponential, but these aspects are most often overlooked, leading to many downstream challenges,” says Sengar.
If data isn’t managed correctly, data analysts cannot use the data accurately. Additionally, if the data storage systems are faulty, breaches of data can create big issues. This is especially true if the data that gets lost or stolen includes customer and client information.
How to Get Into Data Engineering
Education
A degree in computer science, software engineering, or information technology is generally a good starting point for those interested in pursuing a career in data engineering. Students should take courses focused on data architecture and database management to prepare for the day-to-day job of data engineering.
Getting a degree and then finding an entry-level role isn’t the only way to get into this career, though. In fact, many data engineers started in other computer science areas before transitioning to data engineering.
“There are a number of avenues for data engineers, but usually in this day and age its usually highly technical analysts that don’t want to go into data science and want to deal more in systems,” says says Vincent Koc, head of data at hipages Group.
Additionally, Koc suggests that software engineers can easily transition into data engineering because they already understand how to handle the systems and infrastructure.
Certifications
Data engineers typically get certifications to prove they understand two fundamental aspects of the job: the architecture behind data storage (how to build these data warehouses) and the programming language SQL (structured query language).
“There are many certifications in the market, but I believe any one of the cloud-provider (AWS, Azure, Google, IBM, etc) would be great to learn the above two skills,” says Sengar.
These certifications, administered by tech giants like IBM and Meta, can also be used to transition into data engineering. Many of the programs don’t require previous knowledge or experience with data engineering, programming, coding, or application development.
>>MORE: Check out our ranking of the best online coding bootcamps for 2023.
Skills Required for Data Engineering
Some critical soft skills for data engineers include analytical thinking and attention to detail. These engineers should also be motivated to find and solve problems. However, the most important soft skill in data engineering is adaptability.
“Data engineers need to be able to adapt to technology but [also] understand the fundamental patterns and system approaches we see in infrastructure and software engineering regardless of discipline,” notes Koc.
Hard skills for data engineering overlap heavily with those required of software engineers. Data engineers need to be proficient in:
- Programming languages, including Python, SQL, and JavaScript
- Data warehousing and using warehouse platforms like Snowflake, Amazon’s Redshift, and IBM’s Db2 Warehouse
- Operating systems like macOS, Microsoft Windows, Linux, and UNIX
- How big data works in a broader sense and why data organization and management are important
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