Data Engineers and Scientists roles and responsibilities can be quite confusing, particularly with the multitude of disciplines fastly emerging over time.
Before going into the differences, it’s important to have a quick overview on their goals and educational backgrounds that affect their job roles and responsibilities.
Data Scientist and Engineer Objectives
For engineers, the goals are task and development oriented. They are more targeted towards building model data structures and systems that allow it to be properly processed. This ultimately results in creating and developing tables and data pipelines to support analytical dashboards and other customers (like data scientists, analysts, and other engineers). In comparison, data scientists tend to be more question and theory focused. Their end goal is to limit costs and increase profits on projects while maintaining a great customer experience and overall business efficiency. Throughout the process, they analyze, gather support, and develop a conclusion for the questions that come up in their tasks.
Data engineering and data science both require studies in data and programming. In fact, for data scientists in particular, since their work is more research based, having a background that is research-based is an added value. Most employers will prefer to hire a scientist with at least a master’s degree that has some sort of technical or mathematical focus. As for engineering positions, the academic background relies on being a developer which requires a more hands-on and practical experience rather than theoretical knowledge. Master’s degrees such as Computational Mechanics and Modelling are mainly targeted for future data engineers since the programs have multiple axes that are based on applicable projects like numerical modelling of structural behavior, complex and multidisciplinary systems modelling, applied mathematics and scientific computing, mechanical study and design, optimisation and reliability, industrial system management, materials, Sustainable development, and mastery of digital tools.
To summarize, at the core, engineers possess a programming background (Java, Scala or Python) and scientists are usually from math, statistics, economics, or physics background.
Engineers deal with the raw numbers and figures, which might contain human, machine or instrument errors and throughout their projects they need to recommend, and sometimes implement, ways to improve data reliability, efficiency, and quality. They also must understand the various technologies and frameworks in-depth and how to combine them to create solutions to enable a company’s business processes with reliable channels.
Moving to data scientists who usually deal with the data that has passed the first round of cleaning and manipulation. They use the processed information to feed to advanced analytics programs and machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling. They leverage large volumes of figures and facts to answer the business needs by building applied mathematical models. They focus on their math or statistics background with programming to explore and examine information to identify hidden patterns.
Both fields have plenty of career opportunities and scope of work, with the increase usage of IoT and BD technologies, there will be a massive requirement of scientists and engineers in almost every IT based organization. For those interested in these areas, it’s never too late to start.
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