Data Engineering vs. Data Science: What's the Difference?

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The tech industry is full of exciting and demanding careers. Jobs are opening almost faster than applicants can apply and the US Bureau of Labor Statistics estimates jobs in the data science and engineering fields will increase by 32% over the next ten years. Growth of that percentage is much faster than average and exceeds other careers by a wide margin.

Those considering entering the tech industry have a large number of career choices to consider. Data engineering and data science are two of the top-rated and highest-paying careers in this industry. Let’s explore the differences between these two careers, their responsibilities, and how much they earn.

What’s the Difference Between Data Engineering and Data Science?

The line between a data engineer and a data scientist can quickly become blurred. Browsing job listings for both roles often show an overlap in the required knowledge, skill set, and education. Responsibilities in job postings may overlap as well, which can cause confusion about what a data engineer or scientist does or what a company needs to solve its data problems.

Despite these inconsistencies, the roles of a data engineer and a data scientist are very different. Data engineers are meant to develop, construct, optimize, test, and maintain data pipelines and architecture.

A data scientist is entrusted with cleaning and analyzing data, answering questions relating to the data, and providing metrics to solve business problems. Their roles couldn’t be more different, but they often have to work with similar software and maintain an equivalent level of technical knowledge. Let’s explore their differences a little further.

Understanding Data Engineering

Data engineers have a specific and vital role. Their job is to develop, construct, optimize, test, and maintain data pipelines and architecture. The pipelines and architecture data engineers develop are essential for enabling data generation, and it’s the job of a data engineer to ensure the data they gather is optimal for analysis.

Responsibilities

The responsibilities of a data engineer may vary slightly depending on their business’s needs. Still, their primary responsibility is creating and maintaining data pipelines and architecture for gathering data.

Data engineers usually fall into one of three categories: generalist, pipeline, and database engineers. The size of the company you work for will be a significant determining factor in which category your work falls into, but most data engineers have a similar set of responsibilities.

Here are some responsibilities you may see listed for a data engineer:

– Develop, construct, optimize, test, and maintain data pipelines and architecture

– Gather data across multiple sources and organize it into one easily accessible place for analysis

– Identify and implement ways to improve data quality, reliability, and efficiency

– Work with data scientists and data analysts to get the most out of the data

– Write automation scripts to handle repetitive tasks

This is merely a brief example of some of the tasks a data engineer may be expected to handle. You’ll also be expected to ensure the architecture or data pipeline is well-aligned with the requirements of the business and is easily accessible by data analysts and scientists.

Necessary Skills

Data engineers deal with raw data that may contain human, machine, or instrument errors. Their work is very technical, and they need the skills to back it up. As a data engineer, you’ll need to have skills or knowledge in data warehousing, coding, operating systems, critical thinking skills, and a basic understanding of machine learning.

In addition to that, data engineers need to have solid communication and leadership skills. You should be able to communicate easily with your team to solve problems and coordinate with data analysts or scientists. Leadership skills will help you if you end up in a senior position where you’re expected to lead your own team.

Required Background

Most companies require data engineers to have a bachelor’s degree in computer science, software development, information systems, or a related field. In addition to that, you’ll need to be proficient in programming languages such as JavaScript and Python.

It’s also essential for data engineers to have a strong understanding and command of relational database management systems. While most businesses do require a bachelor’s degree, it is possible to get hired without one. Some industry leaders, such as Google and IBM, have stated they don’t require a bachelor’s degree for their tech employees. If you can learn everything you need to know from a coding bootcamp, then you could be hired as a data engineer.

Earning Potential

The earning potential for data engineers is very high. Most data engineers report making between $66,000-$170,000 annually, with a median annual salary of $93,715. You can expect to make closer to $66,000 with an entry-level position, but within a few years, you should see your income get close to the median wage.

There’s a significant increase in demand happening for data engineers at this time, so job security isn’t a concern. Technology is rapidly increasing, and companies need experienced and devoted data engineers to help them keep up.

Understanding Data Science

Data scientists may work with data engineers, but their role is very different. Their role is to clean up the data and use software to find and predict patterns. A data scientist is entrusted with using data gathered by data engineers to conduct research that answers industry and business questions.

Data scientists may also spend a large portion of their time using machine learning, developing models, or incorporating advanced programming to find and analyze data.

Responsibilities

The responsibilities of a data scientist may sound similar to those of a data analyst, but there are some key differences. Data scientists are often considered more senior than analysts and often form their own questions about the data, while analysts usually support teams that already have set goals.

Here are some typical responsibilities of data scientists:

– Answer industry and business questions through research conducted on gathered data

– Prepare data for use in prescriptive and predictive modeling by employing machine learning, statistical methods, and sophisticated analytics programs.

– Clean and validate data to ensure uniformity, correctness, and completeness

– Interpret the data to determine opportunities and solutions

– Communicate their findings to executive levels and stakeholders

This is just a general list of responsibilities and doesn’t fully encompass everything a data scientist may be expected to do. You may also need to use prescriptive and predictive analytics to automate work, devise and apply models and algorithms to mine stores of big data, and more to meet a business’s requirements.

Necessary Skills

Data scientists are entrusted with a lot of sensitive information, and it’s their job to clean up the data, find hidden patterns, and discover opportunities and solutions from the data. To do all this, you’ll need solid skills.

Having solid programming and coding skills is essential for data scientists. You’ll need to be proficient in different programming languages and database software. You also need a solid understanding of advanced mathematics, data analysis and visualization, web scraping, and more.

Aside from technical skills, data scientists should have good interpersonal and communication skills. It’s vital for you to work well with others and be able to communicate the information you’ve gleaned from the data effectively.

Required Background

Unlike many other technical jobs, a data scientist is almost always going to be required to have a bachelor’s degree in a specific field. You may need a degree in mathematics or computer science, and you’ll need to prove you have a solid understanding of programming languages.

A coding bootcamp may get you up to speed on programming languages and related subjects, but most employers will require you to have a degree in certain fields.

Earning Potential

Data science is one of the best-paying careers in the technology industry. Your earning potential depends on your experience level, the company you work for, and your location. Annual salaries can range from $55,000 to over $300,000.

Someone working in Silicon Valley is likely to make far more while working for a major company than someone working in a small town in the southeast. Still, this career will almost certainly set you on a path to financial security.

Do you want to become a data engineer or data scientist without having to go back to school? Check out TECH I.S. to learn how to get started.

Start Your Tech Career With Coding Bootcamp

With major companies like Google and IBM no longer requiring college degrees for their technical workers, it’s entirely possible you can find a job as a data engineer or a data scientist without going through traditional channels. There are alternative routes you can pursue, and a coding bootcamp focusing on data science is an excellent option.

With a coding bootcamp, you’ll reduce the time and expenses necessary to receive certification. Your instructors will teach you everything you need to know and help you prepare for jobs with mock interviews and resume preparations.