Thanks to the continuous growth of digital and cloud-based technologies, we are living in a world that is driven by data. It is estimated that around 1.145 trillion MB worth of data is generated every day.
Due to this immense amount, many data-related job roles have emerged in order for companies to capitalize on managing and analyzing their data. Some of the biggest companies in the world, such as Google, Facebook, Netflix, and Amazon, invest in data to create a better customer experience. So if you searched the meaning of a word while reading this article, Google will take note of that search and suggest similar topics to you in the future.
Data goes through many stages, from raw format to the point where we can understand and interpret it. To process the data, a network system known as a data pipeline is required. This system allows data to be moved from a source location to a target location. The first stage of the data pipeline is gathering the data, then comes engineering, and finally loading it into a data warehouse system.
An important topic to discuss, if you are thinking of entering this field, is the difference between various data-related jobs. In this article, we will be focusing on the difference between a data engineer and a data scientist. But first, let’s establish what these two professions entail.
What Is a Data Engineer?
A data engineer is a professional who sets up and prepares data infrastructure to be used for data analysis by data analysts and scientists. The focus of data engineers is on the format, security, resilience, and scaling of the data.
What does a data engineer do?
Throughout a normal working day, a data engineer primarily organizes, processes, and stores the collected data from different sources.
Professionals of data engineering also are expected to create data tools for the other data-related teams, such as analytics tools that make use of the data pipeline in order to provide insight into the customer’s searches and purchases. They help with data-related technical issues as well as create and support data pipeline architecture.
Data engineer career path & salary
The career path of a data engineer can vary, depending on the person’s prior experience in engineering, as well as on the organization they work in and the roles they offer. However, generally, as a new data engineer, you might begin work as an intern for data engineering, a regular software engineer, or even a data analyst. Then, once you become an entry-level data engineer, you can progress to senior-level, lead data engineer for your sector, all the way to executive roles such as the head of data engineer or chief data officer.
On average, data engineers earn $127,506 per year in the United States. The highest-paying cities for data engineers are:
- San Francisco, CA: $164,945 per year
- Los Angeles, CA: $147,911 per year
- New York, NY: $146,717 per year
- Plano, TX: $138,201 per year
- Austin, TX: $138,041 per year
Data engineer skills
Data engineers are required to have a strong understanding of software engineering and the basics of distributed systems. They must be knowledgeable in algorithms and data structures, as well as be able to work with a few programming languages (Python, Java, and Scala) that are used for statistical modeling and analysis, building data pipelines, and data warehousing solutions.
Some of the programs that data engineers should be familiar with are:
- Apache Hadoop and Apache Spark
- Amazon Redshift
- Amazon s3
Other skills that hiring managers value in data engineers include:
- Communication skills
- Collaboration skills
- Presentation skills
What Is a Data Scientist?
A data scientist is a professional who designs various data modeling processes and creates algorithms and predictive models in order to perform custom analyses of the data prepared by the data engineers. The focus of data scientists is to find patterns, detect anomalies, and apply the knowledge extracted from data to solve problems in various domains.
What does a data scientist do?
Throughout their working day, data scientists perform a number of duties regarding analyzing data, creating statistics, and using programming to mine large sets of data.
Data scientists conduct high-level research so they can identify patterns and current trends. Their job is to use the knowledge gathered by the research to offer the greatest marketing and business opportunities to the company. They work closely with data engineers, analysts, and architects to build and support various databases, analyze the data, and communicate business insights.
Data scientist career path & salary
The general career path of a data scientist begins with earning a bachelor’s and/or master’s degree in computer science or a related field. Then you could get an entry-level job, such as a data analyst or junior data scientist. Then you can progress and get promoted to a senior-level data scientist, the head of your department, and other executive roles.
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The average salary for a data scientist in the USA is $119,696 per year. The highest-paying cities are:
- San Francisco, CA: $156,441 per year
- Santa Clara, CA: $156,284 per year
- New York, NY: $140,106 per year
- Austin, TX: $131,078 per year
- San Diego, CA: $124,679 per year
Data scientists skills
Data scientists must be knowledgeable in mathematics and statistics, as well as have an understanding of Python and R programming. These programs are used for data mining, manipulation, calculation, graphical display, and running embedded systems. Data scientists should be able to perform statistical analysis and extract information that helps companies make better decisions regarding their marketing strategies and help deliver better products and services. They need to be proficient in using statistical modeling software, such as SQL databases and the Hadoop platform. Other skills that employers value include:
- Communication skills
- Critical thinking skills
- Collaboration skills
Data Engineer vs. Data Scientist
The matter of data engineer vs. data scientist has been an ongoing debate whenever the field of data science is discussed. To understand the difference between these two roles, we must first establish data science versus data engineering.
Data science vs. data engineering is like theory vs. practice. To illustrate, let’s say that a company keeps getting their products returned from the customers. In order to solve this problem, they turn to the data that is gathered by data engineers continuously. They must analyze which items were bought and returned, the locations from which they were distributed and then returned, the time of day of the purchase, the price, and so on. There are two parts to solving this problem, one dealt by data science and the other by data engineering.
- Data scientists are responsible for designing the models that guide them towards the solution.
- Data engineers then implement that design and ensure that it functions well.
Although there is some overlap between these two positions regarding the programs they use, The key is to understand that data scientists and data engineers work differently with data. To put it simply, data scientists are the ones who design data models, whereas engineers develop and construct the systems used to manage and transform the raw data into usable formats.
Both careers require formal education and a specific set of skills to fulfill the daily responsibilities that data scientists and engineers have. With the right training, you can even transition from one job to the other.
Now that we have explored both these careers in more detail, if you do decide to pursue one of them, take into consideration the necessary skills, your personal interests, and the desired salary. Both are great career choices, and with a combination of the right skill set and experience, you will have guaranteed success in whichever one you choose.