A lot has been said about the difference between various data science roles. Terms like ‘big data’ have begun to spark interest in graduates looking to pursue careers in data science. Back in 2012, Harvard Business Review named data science the “Sexiest Job of the 21st Century”, and suddenly everybody wanted to be a data scientist. This perspective has begun to change over the last couple of years as new roles in the field have emerged. Data engineering is one such field born out of data science.
This article discusses the major differences between data scientists and data engineers, with a focus on their roles and responsibilities, educational backgrounds, and salaries. The purpose of this post is to help you get started in either data science or data engineering.
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What Is a Data Engineer?
Data engineering is one aspect of data science and focuses on the practical applications of data collection and analysis. The job of a data engineer involves harvesting big data, including creating interfaces that facilitate access to information and its flow. Unlike data scientists, their role does not include experimental design or analysis.
A data engineer’s job involves developing, constructing, testing, and maintaining databases and large-scale processing systems. Data engineers handle raw unformatted data that may contain machine, instrument, or human errors, or may contain system-specific codes. Therefore, you’ll need to have expertise in programming, system architecture, database design and configuration, and interface and sensor configuration. It’s crucial for data engineers to have a strong understanding of concepts such as relational and non-relational database design, information flow, logical operations, comparative analysis of data stores and data models.
What Is a Data Scientist?
Data science is an interdisciplinary branch of computer science that exploits large amounts of data and uses different scientific processes to study and analyze them. Data scientists deal with various kinds of data—structured, semi-structured, and unstructured—employing technologies like data purging, data mining, and data transformation.
Data scientists interact with data infrastructure built by data engineers. The responsibilities of a data scientist include conducting high-level business and market operation to recognize the patterns and relations in the market. Data scientists depend on data engineers to get the data built, then they work on it by analyzing the data using tools like Hadoop, SPSS, Oracle, MySQL, Hive, and Cassandra. They’re analytical experts who use their technological skills and knowledge of social science to manage and analyze the trends and patterns of data. They also use their industry knowledge, skepticism, and assumptions to find solutions to anticipated challenges in the business.
How to Begin a Career as a Data Scientist
The basic foundation for careers in both data science and data engineering are similar. If you want to pursue a career as a data scientist, the first thing you need to do is narrow down your areas of interest. If big data analytics fascinates you, you should consider pursuing a bachelor’s degree in data science along with statistics, calculus, and linear algebra as your complementary subjects. After that, you may want to apply for an internship or enroll into a master’s program in data science or a related field.
An internship before a postgraduate program can be beneficial for aspiring data scientists or data engineers, as you’ll get a glimpse into the workings of a professional organization and the potential role you’ll play in it. The skills you pick up will also help you to refine your future areas of interest.
Completing your master’s program may help you to secure a role at a larger company, as this establishes a strong academic base along with your preliminary work experience. But if you’re looking to gain job-ready skills in a shorter amount of time, then a data science bootcamp is a more affordable and flexible option.
Traditional degrees aren’t for everyone. They provide an excellent foundation for further academic study but are time consuming and expensive. A bachelor’s degree in statistics will take four years to complete and cost up to $30,000 per year.
For a more practical approach to learning, you may want to consider our data science bootcamp. Designed by industry experts, these online courses are more intensive, more focused, and more affordable that degrees. Our primary goal is to get you hired at a top data firm, fast. You’ll gain hands-on experience working in teams solving big data problems.
How to Begin a Career as a Data Engineer
If you’re interested in pursuing a career in data engineering, consider enrolling in a bachelor’s degree in computer science, or a related field like applied mathematics, computer engineering, or statistics. You’ll need to work hard to polish your skills in SQL, which is the most crucial programming language for data engineers, along with an understanding statistical analysis and modeling tools like Python.
Once you’ve completed your bachelor’s program, you might want to take up a temporary job that’s relevant to your field for the sake of experience, even if it doesn’t involve engineering. This will provide you with valuable insights about the profession. You can also advance your career by completing a master’s program or a certification from Microsoft, Oracle, or IBM. While a master’s or certification could help you stand out, many companies favor specific hard skills and a strong portfolio - all of which you can gain through an online data science course.
How to Succeed as a Data Scientist
It’s important for data scientists to understand the goals of the business they’re working for. That’s why data scientists work closely with other business stakeholders - perhaps even more so than data engineers.
Data scientists determine how to use big data to achieve the business’ goals and move the operation forward. To succeed as either a data scientist or data engineer, you should be flexible and adaptable, open to new ideas, and have the ability to develop and propose innovative solutions.
A data scientist may have to work with different teams of an organization. Having an awareness of the decision-making that takes place within different departments will help you to focus your efforts on the data projects that will in turn shape further decision making in the company. You should also pay attention to the kind of data the company intends to collect or discard. With experience, you’ll develop a stronger understanding of customer behavior and how to effectively use data for further customer satisfaction, which is a priority for all businesses.
Both data scientists and data engineers rely on similar sets of hard skills and soft skills to excel.
How to Succeed as a Data Engineer
A data engineer’s primary responsibility is to manage the enormous flow of data and its extract, transform, load (ETL) processes and pipelines.
You’ll need to have a sound knowledge of SQL and Python and be able to work with cloud platforms, especially Amazon Web Services. Giant IT companies like Google and Amazon require their data engineers to have experience with big data programs like Kafka, Hadoop, and Spark, and to have a strong understanding of data structures and algorithms.
Apart from being excellent in programming, you should be well versed with the fundamentals of databases, including how to plan, operate, design, and troubleshoot.
You’ll need to learn how to design efficient relational databases from scratch. Scaling is a huge challenge when working with big data. A database needs to utilize indexes, and avoid redundant relationships to maximize speed.
Difference in Salary Data Scientist vs Data Engineer
There’s no arguing that data scientists bring a lot of value to the table. That’s why data scientists are some of the most well-paid professionals in the IT industry. According to Glassdoor, the average salary of a data scientist is $113,436. All industries have now realized the power of big data and want to use it to shape their business decisions. As it’s still a fairly new profession, demand for data scientists is high. O’Reilly’s 2016 Data Science Salary Survey reveals that experience is one of the key factors in a data scientist’s salary. For every year of experience, a data scientist earns $2,000 to $2,500 more.
Glassdoor reports an average annual compensation of $137,776 for data engineers. Depending on their skills, experience and location, a data engineer can earn an average salary ranging between $110,000 and $155,000.
Meanwhile, senior data engineers earn an average of $172,603 per year, with a reported salary range of $152,000 to $194,000.
There are also a number of different employee perks and benefits available for these in-demand roles. These range from extra maternity and parental leave, flexible working hours, remote working options, and company share bonuses.
Data scientists and data engineers need to work together to tackle business problems and provide useful insights that will help to inform high-level decisions. Both roles are complementary and cannot be performed independent of each other. The demand for experts in these fields will continue to be high for a long time. If you’re interested in a career in data science, Thinkful offers an accelerated online program of classes, mentorship, and professional guidance through our full-time Data Science course. If you need a more flexible option, try a part-time Data Science course and work at a pace that suits you.You can read more about exciting opportunities in related fields on Thinkful’s software engineering blog or data science blog.
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