Data Engineer vs Data Scientist: Which One Do You Need?

A data engineer is a person who makes and looks after the systems that gather, store, and study data. They create and build pathways that turn raw data into useful information for data scientists and other important people in a company. These engineers make sure that data systems work well and smoothly. Their jobs include developing, testing, and keeping up data structures, building data pathways, and improving data quality. They can be generalists, focus on pipelines, or concentrate on databases, depending on what their job needs. Understanding what is a data engineer is crucial for building a strong data team.

Data scientists study and understand complex data to help organizations make smart choices. They use methods from statistics, machine learning, and data visualization to find patterns and insights in the data given by data engineers. Their jobs include analyzing data, running experiments, testing ideas, and making predictions. Data scientists can focus on areas like machine learning, actuarial science, statistics, or digital analytics. They are also good at showing their findings in visual ways to make complicated data easy for others to understand. Knowing what is a data scientist will help you leverage their skills effectively.

Focus and Objectives

Data engineers mainly work on building and keeping the systems for making, storing, and getting data. Their main goal is to create efficient and reliable data pathways and structures. This means making sure data moves smoothly from where it starts to where it needs to go, keeping data quality high, and making sure the system works well. They focus on creating strong systems that can handle lots of data safely and quickly.

On the other hand, data scientists focus on studying data to find useful information. Their goal is to understand complex data, create models to predict outcomes, and help make smart decisions. They look for patterns and trends in the data, giving valuable insights to guide business strategies. Data scientists use statistical methods and machine learning to analyze and show data in a way that decision-makers can easily understand.

Responsibilities

Data engineers design and build data pipelines and systems. They make sure the data is high quality and work on combining and transforming data. Their job is to create strong systems to handle large amounts of data, making sure it is clean, reliable, and ready to be analyzed. They also work with data scientists and other important people to ensure the data systems meet business needs.

On the other hand, data scientists study and understand complex data. They build and test models to predict future trends and share data insights through visualizations and storytelling. Data scientists run experiments and create ideas to understand how data behaves. They use statistics and machine learning to make models that show future patterns. They also communicate their findings to others, making complex data easy to understand and use.

Skills and Qualifications

Data engineers need to know a lot about managing databases, ETL (Extract, Transform, Load) processes, and big data technologies. They should be good at building and maintaining data systems, making sure the data is high quality, and improving data pathways for better performance. Data engineers often have backgrounds in computer science or engineering and are skilled in software development and system design.

Data scientists need to be experts in statistical analysis and machine learning. They should have strong problem-solving skills and be good at thinking critically. They must be able to communicate complex data insights clearly. Data scientists use tools and languages like Python, R, and SQL for data analysis and modeling. They usually come from fields like statistics, mathematics, computer science, or economics. Good communication skills are important for them to explain their findings to others.

Languages & Tools

Data engineers typically use languages like SQL, Java, Scala, and Python. They rely on tools like Hadoop, Spark, Kafka, and Apache NiFi to build and maintain data systems. These tools handle large amounts of data, ensuring quality and performance. For example, Hadoop and Spark are used for processing data across many computers, while Kafka and Apache NiFi are used for data integration and managing real-time data flow.

Data scientists, on the other hand, commonly use languages like Python, R, and SQL. They use tools like TensorFlow, Scikit-learn, Pandas, and Jupyter notebooks for data analysis and modeling. These tools help with statistical analysis, machine learning, and data visualization. For example, TensorFlow and Scikit-learn are used for building and training machine learning models, while Pandas is used for organizing and manipulating data, and Jupyter notebooks for interactive data analysis and visualization.

Salaries and Hiring Projections

Data engineers have an average base salary ranging from $90,000 to $126,000 in the U.S. and $42,000 to $84,000 in Latin America. The demand for these roles is increasing as many companies in the U.S. and Latin America need to build and maintain strong data systems. Consequently, data engineers are crucial in making sure data systems are efficient, scalable, and able to handle the growing amount of data businesses produce.

On the other hand, data scientists earn between $110,000 and $151,000 in the U.S. and $36,000 to $60,000 in Latin America. The demand for these roles is also expected to grow as organizations use data for strategic decision-making and gaining a competitive edge. As a result, data scientists are highly valued for their skills in understanding complex data, developing predictive models, and providing actionable insights that help shape business strategies.

Career Projections

For data engineers, the career path usually starts with entry-level jobs that focus on bringing in and organizing data. As they gain experience, they move to mid-level positions where they handle complex projects and design data systems. Senior-level engineers lead big data projects, improve data pathways, and may eventually become managers or executives, like Chief Data Engineer. Data engineers often advance by learning more about system design, working with large amounts of data, and using big data tools.

For data scientists, the career path often begins with entry-level roles that focus on analyzing data, cleaning it up, and doing basic machine learning tasks. As they get more experience, they can move into mid-level roles where they lead data projects and work with different departments. Senior data scientists solve complex business problems, help junior team members, and may eventually become managers or executives, like Chief Data Scientist, where they guide data-driven decisions for the whole organization.

Involvement in Projects

Data engineers are important at the start of tech projects. They set up data systems to ensure data is easy to access and high quality. They build systems that enable smooth data flow, providing a foundation for projects that rely on data. Data engineers focus on creating strong data architectures that can handle large amounts of data efficiently and securely, making sure the data is clean and ready for analysis.

Once the infrastructure is set up, data scientists take over. They use the prepared data to develop models, perform analysis, and provide insights that guide project decisions and strategies. Data scientists analyze and interpret the data, finding patterns and trends that can help make smart decisions and drive business success. They use statistical methods and machine learning techniques to understand the data and give useful insights to stakeholders.

Choosing the right data talent is crucial for your organization’s success. Remember, the synergy between data engineers and data scientists is what drives innovation and efficiency in your data strategy. Data engineers lay the groundwork by building robust data infrastructures, while data scientists turn data into actionable insights that can propel your business forward.

If you’re looking to hire top talent from LatAm, whether it be in tech, finance, marketing, or more, we can guide you through the entire hiring process and find that perfect fit for your team. Reach out to us today and we’ll help you get started.