Big Data Developer vs. Data Engineer: What's the Difference?

Data analysts and scientists do their best work only if they’re using proper and accurate data. Data engineers compile and produce high-quality data for the rest of the team to use. As the need for data scientists continues to grow in the coming years, the demand for quality data engineers to support their work should also skyrocket.

Perhaps you’re thinking about going into a role in data engineering or big data development. Or, maybe you’re preparing to hire a data engineer and you want to learn more about the best practices and standards in the industry. In this article, we’ll break down the difference between big data developers and data engineers to help you see how each role is different and why they’re both important.

Table of contents:

• What is a data engineer?
• What is a big data developer?
• Data engineer vs. big data developer
• Future trends and outlook
• Explore your path in big data

What is a data engineer?

Data engineers create and maintain systems that can store and process large amounts of data. The field itself is rather broad, and data engineers are needed in a variety of different industries. Their main goal is to ensure that data is accurate and usable by the time it reaches those who will be studying and analyzing it.

Data engineer roles and responsibilities

Data engineers improve data quality and efficiency by understanding the problem at hand and defining clear goals for the solution. They play a key role in data management and processing, which involves testing and refining data pipeline architecture in addition to building algorithms that transform raw data into a comprehensible form. This equips them to make better predictions and models in the future.

Other key topics of importance for data engineers include:

• Extract, transform, and load (ETL) processes. ETL processes include the steps taken to combine data from various sources into a central hub, often referred to as a data workshop. This system gets data ready for storage and also prepares data for use in analytics and machine learning.
• Data modeling and data warehousing. Before data can be used and stored, it must be analyzed and processed. Through data modeling, data engineers uphold best practices, including using name standardization and building organized structures.
• Data pipelines and data infrastructure.  A data pipeline is a series of steps that data follows to become usable or storable. Typically, this includes three major components: a source, a processing step (or series of steps), and a final destination or conclusion.
• Real-time data processing. Real-time updates will keep the entire team moving forward while enhancing agility, efficiency, and shared understanding.

Technical skills required  

Given the unique complexity and intricacies of data engineering, data engineers must possess several technical skills. First of all, data engineers should have prior knowledge and experience working with programming languages such as Python, Java, and Scala. Data engineers need to be well-versed in these types of code to troubleshoot issues and improve systems.

Data engineers should also understand how to incorporate structured query language (SQL) and not only SQL (NoSQL) relational databases. These databases are valuable when building data integration scripts and facilitating various data pipelines. Another key skill is the ability to utilize cloud platforms such as Amazon Web Servicing (AWS) and Azure. These cloud platforms make data more accessible for all stakeholders while also keeping all your important information safe and secure.

One other skill worth mentioning is the ability to work with application programming interfaces (APIs). APIs enable multiple software applications to communicate back and forth with each other in real time. Implementing APIs will help with real-time data updating and integration.

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What is a big data developer?

You now know a little more about what data engineers do, but what about big data developers? As you’ll see below, the roles are similar—but certainly not identical. Big data developers are tasked with building applications that use the data analyzed and processed by the data engineers. Let’s dig more into what this encompasses below.

Big data developer roles and responsibilities

Handling large datasets is the fundamental responsibility of big data developers. The specific amount can vary depending on who you’re asking but typically includes at least ten terabytes of data. Other attributes of big data in addition to the amount include the variety of data stored and the velocity at which it can be accessed.

The job of a big data developer includes data transformation and data storage tasks. They are the ones who create and maintain data pipelines to put the data to good use. They may even work with data engineers to define and implement the process of data cleansing, transformation, and storage—although the data engineer will be the one responsible for making these plans happen. The end goal of each of these tasks is to create reports on the data and build scenarios where data can work together.

One way big data developers process data is through data mining, or the act of working through large sets of data to identify patterns and commonalities. These findings enable businesses to predict what trends may arise in the future, which can aid in making informed business decisions.

Another key responsibility of data developers is data architecture, a process for managing data’s flow through various systems. Data architecture is what enables the creation of various processing functions as well as applications that use artificial intelligence (AI). Through data architecture, data developers can define the process and framework necessary for extracting, transforming, and loading data that data engineers will later implement. They may also incorporate data mesh and data fabric architectures to eliminate complexity and better process data.

Big data developers play a key role in data visualization for stakeholders by representing data in charts, graphics, and other visual elements. The purpose of data visualization isn’t only to express what’s going on but to provide an interpretation of the data in a meaningful way that supports decision-making and future planning. These functions are especially helpful for business leadership as they strategize how to reach their objectives across multiple departments and gain the competitive edge within their industry.

Finally, big data developers ensure systems are scalable to handle growing data volumes. As they work on these systems, they are mindful of the value of real-time data processing. Additionally, they incorporate specialized tools into these systems to ensure the ongoing reliable collection and processing of data.  Even when the data is coming from several different sources, the systems can effectively manage, integrate, and analyze the datasets).

Technical skills required

You can use several  tools for big data development, including ETL processes, and each has its own advantages and disadvantages. These technologies can help when extracting data from various sources and combing through data to ensure the highest possible consistency and quality. Enterprise software big data tools are usually the most robust, which means they come with the highest price tag. Open-source ETL tools are sometimes more accessible, but they may lack the same level of upkeep and ease of use.

Like data engineers, big data developers should also have some familiarity and past experience working with Python, Java, and Scala. Scripting languages also play a role in automating tasks. Frameworks like Hadoop, MapReduce, and Kafka are also useful data management infrastructure tools that help facilitate data scalability through open-source code.  

Data engineer vs. big data developer

Data engineers and big data developers share many tasks. They must both collaborate with data scientists and data analysts. Additionally, data engineers and big data developers are both responsible for looking for patterns in data to solve problems and create solutions. They also share knowledge of coding languages such as Python, including becoming certified.

Although both roles contribute to meeting the needs of stakeholders, important differences set data engineering and big data development apart. Engineers are more involved in the early stages of data analysis, while developers are more apt at applying the engineers’ findings to resolve issues or inform decision-making.

Education and skill sets

Usually, data engineers and big data developers need to have a bachelor’s degree in a related field like computer science, information technology, or data science. A master’s degree isn’t required, but it can lead to higher-paying positions and more opportunities for career advancement.

Regardless of the focus of your degree, several essential skills are helpful for data engineers and big data developers to have. Generative engineering skills and natural problem-solving abilities are valuable assets in this field. Having a deep knowledge of algorithms and structures as well as a basic understanding of distributed systems that use coordination protocols and message brokers also helps.

Career path and job titles

When you look at a job description for a role in data engineering or big data analytics, you’re likely to see responsibilities that involve managing ETL processes, defining governance policies, and analyzing large pools of data. Requirements may include past experience with coding languages and data engineering skills for cloud platforms.

Not all data engineers or big data developers have the same title, as these are both wide-encompassing fields. Data engineers can find jobs as data architects, machine learning (ML) engineers, data warehouse engineers, and solutions architects. Big data developers may work as database managers, technical recruiters, and security engineers. The extensive training and education for these positions pays off, as a number of these roles come with an average salary higher than $100,000.

Keep in mind that your career isn’t limited to your first position. As your career goes on, you may look for opportunities to further specialize or focus your work. If you’re a data engineer, this could mean pursuing a role in analytics engineering or data governance. For big data developers, specialization might look like becoming a data visualization developer or analytics consultant.

Tools and technology

Data engineers and big data developers both use specific tools in their roles on a daily basis, but the tools they incorporate are not the same. Data engineers are more likely to use Python, while big data developers typically find more value in Apache Spark and data visualization tools. However, both positions implement SQL on a regular basis.  

As you build your personal tech stack, imagine where you see yourself working in the future. Think about the specific roles and tasks involved in each position and consider which path best suits your natural skills and interests.

Future trends and outlook

As time goes on, new trends in data engineering and big data development will continue to emerge. Right now, data lakehouses and open table formats are hot topics of conversation in the field of data engineering. Both tools impact how data is stored and processed. In big data development, many are curious to see how enabled federated search and a wider adoption of robotic process automation (RPA) technologies impact the industry.

Then, of course, there are questions about how roles will evolve with advancements within AI and machine learning. Although AI won’t replace data engineers or big data developers, many within the industry believe it will improve the work they are doing. Because AI is evolving so quickly, it’s essential to continue to stay on top of new trends and advancements as you advance in your career.

Although these positions are competitive, there’s no shortage of opportunity. Employment of data architects and administrators is likely to grow quickly, as evidenced by the rapid rate of growth of data scientist jobs. Around 18,000 new job openings are projected annually, mainly from an increased demand for data-driven decisions and analysis.  

Explore your path in big data

Big data developers and data engineers are both working toward the same end goal, even if their roles and responsibilities look slightly different. Data engineers are responsible for compiling and processing the data on the front end, while data developers are more focused on the interpretation and application of the data once it’s gathered together. As you think about which career path might be the right option for you, consider how your unique skills, experience, and interests align for the best possible fit.

Upwork is a great tool for aspiring professionals and companies looking to hire. If you’re searching for your next data engineer or big data job, you’ll find plenty of full-time and independent talent positions accepting proposals on Upwork. You can also use Upwork to hire big data developers and data engineers. Check it out today!