Data Engineer Vs. Data Scientist Vs. Data Analyst

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Jul 23

AI without data is like a car without fuel: powerful but useless.

With the rise of AI, companies cannot fully leverage AI without data roles like engineers, scientists, and analysts. These roles are essential for collecting, preparing, and analyzing data, which forms the foundation for training and maintaining accurate AI models. Without skilled data professionals, the potential of AI cannot be fully realized.

According to the World Economic forum’s Future of Jobs Report 2023, Economic, health, and geopolitical trends have created divergent outcomes for labor markets globally in 2023. Technology adoption remains a key driver of business transformation, with 85% of organizations focusing on new technologies and digital access. Big data, cloud computing, and AI are expected to be adopted by over 75% of companies in the next five years, significantly impacting job markets. While automation progresses slower than anticipated, human-machine task division is evolving, with 42% of tasks expected to be automated by 2027. The largest job growth is anticipated in technology, digitalization, and sustainability roles, such as AI and Machine Learning Specialists, and Sustainability Specialists. At 58%, Big Data Analytics is ranked first the technology that will be the biggest creator of jobs.

In my opinion, these “Data” based roles can be broadened into 3 main buckets: Analysts, Scientists, and Engineers. The skills of these roles can easily overlap, but what makes them distinctly different is their focus.

Data Analyst

A person who specializes in making sense out of past and current numerical data to find answers to business questions and help business leaders make better decisions. (Also known as a Business Analyst when applied to business).

Focus: Storytelling, trend analysis, presenting business simulations, understanding business requirements, creating visualizations.

Data Scientist

A person who specializes in building analytic and predictive models (with data received from data engineers) to interpret complex data.

Focus: Applying statistical/machine learning tools to classify patterns, determining strength of patterns and relationships, quantifying cause-and-effect, training and optimizing machine learning models.

Data Engineer

A person who specializes in building, testing, optimizing, and maintaining the data ecosystems that allow data scientists and analysts to perform their work.

Focus: Designing the big data infrastructure and preparing it to be analyzed, building complex queries to create “pipelines”, cleaning data sets, and arranging problems (typically given by data scientists) in the programmed system.