Historically, businesses relied heavily on intuition to make almost all decisions, including those critical to a company's survival. Today, businesses can’t afford to "go with their gut," as they have the opportunity to capture and rectify information to make intelligent, strategic, and quantifiable decisions.
Big data is used in almost every industry—from sales and marketing to food, health, and even space exploration—so when we say "big data," we mean it. The Square Kilometre Array used in space exploration, for example, generates an astonishing 700 terabytes of data per second. That is approximately 100 times the amount of data produced by the internet operating at maximum capacity.
All of this data is meaningless if it cannot be refined and analyzed. Fortunately, the specialized roles of data engineers and data scientists are equipped with adequate proficiency to manage a huge quantity of info.
This article will discuss data engineers vs. data scientist roles in detail, including the job description and the state of their job market. Let’s get straight to it.
Data Science vs. Data Engineering: What Is the Difference?
People are frequently confused when comparing data science vs. data engineering, as both generate invaluable insights. Still, the primary distinction between the two is the scope of their responsibilities.
To grasp the role of each one, let’s imagine a large corporation. The data engineer’s task is developing software to generate the necessary data and ensure it can be found in the right place at the right time. A data scientist’s job is to get viable details or "make sense" out of this data. They help companies draw conclusions based on the information they analyze.
These jobs go hand-in-hand. The role of a data engineer is to obtain and consolidate raw data. Doing this subsequently enables the data science team to dig into a problem and set up the groundwork for optimizing the company's operations.
Let's go further and compare each role separately.
Data scientist positions are derived from several technical fields, including computer science, math, and statistics. Their primary responsibility is the analysis and interpretation of huge volumes of data to assemble and train predictive models and relay their insights to the company's managers and execs (e.g., predicting customers’ buying patterns and pinpointing new revenue opportunities).
The data scientist's task is to collect and analyze information with the help of analytical software and reporting tools to discover patterns, trends, and relations in large data units. They also include, but are not limited to, the following:
Data handling, mining, cleaning, and preparation for practical use
Creating models capable of operating on big data as well as analysis and interpretation of obtained info
Taking the position of team leader and assisting their team in achieving objectives
Delivering results that have a direct impact on business performance
Analytical skills - Data scientists sift through and make sense of massive amounts of information and must be capable of identifying patterns and trends while comprehending their significance.
Communication skills - Data scientists are responsible for not only analyzing data but also explaining it to others who frequently lack the same level of technical knowledge. Comprehending complex technical issues and the significance of data patterns can be challenging; thus, communication skills come in handy.
Mathematics, programming, and technical skills - While data scientists benefit greatly from soft skills such as analysis, creativity, and communication, they also require exceptional math knowledge (especially in multivariable calculus and linear algebra). Additionally, they must be able to program in one or more programming languages (Java, R, Python, or SQL).
Indeed.com reports that data scientists' average yearly salary in the U.S. is $115,885. However, they can negotiate a significantly higher salary by demonstrating critical skills such as cloud architecture, DevOps, micro services, and performance marketing.
State of the job market
Data scientist was the second-best job in America in 2021, per Glassdoor's ranking. The demand for data scientists is through the roof and shows no signs of running out of steam. As the U.S. Bureau of Labor Statistics predicts, the data science field will grow at a rate of approximately 11.5 million new jobs by the end of 2026.
Data engineering positions frequently require work in a variety of environments to develop systems for collecting, managing, and making data more comprehensible for other data stakeholders. Their ultimate goal is to enable corporations to access info in order to assess and optimize their performance.
Generally, data engineering entails developing data models, constructing pipelines, and supervising ETL (extract, transform, load). That is why some of their responsibilities include the following:
Extracting insights from data via data mining
Conversion of erroneous data to a form suitable for analysis
Creating, testing, and maintaining architectures for database pipelines
Developing novel data validation techniques and analysis tools
Ensuring data quality so that it can be further managed by data scientists
Creating large data warehouses with the assistance of additional transform load (ETL)
Programming and data engineering skills – Just as mechanical engineers must combine mathematics, physics, and materials to design complex mechanical systems such as engines, data engineers must leverage their programming skills to construct software solutions that efficiently collect and analyze raw data.
While some exploratory data analysis/big data tools and systems are written in Java (Hadoop, Apache Hive) or Scala (Kafka, Apache Spark), Python has begun to replace both lately. Data engineers also widely use SQL, Rlang, and Golang, which started to gain popularity within the profession.
Creating databases/warehouses – A data engineer's responsibilities include designing and constructing data storage systems. Because they frequently work with specialized tools, they know such programming languages as SQL and NoSQL.
Soft skills – Soft skills such as public speaking, interpersonal communication, project management, moderating, and documenting are often overlooked by data engineers who seek employment. However, employers need professionals who are well-rounded and capable of teaching teams and taking the initiative on their own, and that's why these skills are also critical.
In the U.S., the median yearly salary for a data engineer is $120,697. Engineers with CAP certifications earn approximately 40% more than that.
State of the job market
The data engineering career path does not always begin with an entry-level position. Numerous data engineers begin their careers as software engineers and progress to solutions architects and machine learning engineers.
Furthermore, the Dice 2020 Tech Job Report labeled data engineer as the fastest-growing job in technology in 2019, with a 50% year-over-year growth in the number of open positions, along with a projected growth of more than 67% in the next ten years.
Now that we’ve reviewed the two jobs in detail, let’s look at some examples of data engineering and data science and check how they can contribute to companies’ success.
A famous data engineer, Dennis Hume, works closely with a data analytics team to make sure they are all on the same page and can leverage each other’s work on all company projects. As Hume explains, the role of data engineers is to support data stakeholders throughout the company.
However, without the full picture and proper explanations, all the gathered data would be in vain. That is where data scientists step in, as they help companies interpret information and solve complex issues thanks to their expertise in a variety of data niches.
Take Dean Abbot, a renowned data scientist, for example. Since 1987, he has developed techniques and methods for data mining, data preparation, and data visualization. As a result, he helped numerous companies handle fraud detection, risk modeling, and even personality assessment.
Businesses of all sizes and sectors are now looking for big data experts to help them gain relevant business insights and make sound business decisions within and outside their organization. They do, however, require specialists with the superpowers of interpreting and converting raw data into these priceless insights. That job is done by data scientists and data engineers.
The distinction between these two roles is frequently blurred, but we hope this article will help navigate through their skills and responsibilities to distinguish the differences between data scientists vs. data engineers.
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