Data engineer vs data scientist: Which role is best for you?

Data engineer vs data scientist: Which role is best for you?

When thinking about big data roles and which might be the right fit for your future career, it’s worth knowing the difference between a data engineer and a data scientist. These two positions are both in high demand, as the world increasingly becomes digitalised, relying more and more on data.

Data engineer vs data scientist: What do the roles entail?

Data engineer

The focus of a data engineer is to build and maintain data infrastructure, including the systems to collect, store and analyse data. They make it accessible to other parts of the business, who use it in their day-to-day roles.

Key data engineer responsibilities include:

• Designing and maintaining pipelines to move data between systems 
• Creating and improving data stores so they are efficient and reliable 
• Ensuring data quality
• Developing algorithms to transform raw data into actionable insights
• Building new data validation methods 
• Developing data analysis tools
• Writing business intelligence reports 

Data scientist

A data scientist specialises in analysing data to derive insights, enabling businesses to make better, well-informed decisions. Much of this is done through statistical methodology, but also through computer science and business principles.

The main data scientist responsibilities are:

• Using software, machine learning and artificial intelligence to perform data analysis
• Creating models and algorithms to forecast outcomes
• Improving data quality
• Communicating insights and recommendations to other departments and stakeholders
• Automating data systems to make processes more efficient
• Ensuring information management meets data security standards

Skills and tools required for each role

Data engineer skills and tools

• Structured Query Language (SQL)
• Extract, transform, and load (ETL) tools
• Cloud platforms like Amazon Web Services (AWS) and Google Cloud Platform (GCP)
• Data warehouse
• Machine learning

Data scientist skills and tools

• Python
• Machine learning frameworks like TensorFlow and Scikit-learn
• Predictive analytics
• Problem-solving skills
• Technology, mathematics, business and communication abilities
• Familiarity with large datasets
• Statistical modelling languages like Hadoop, R, and SAS

Career opportunities

A typical data engineer career path is likely to start with entry-level roles focused on building data pipelines and managing databases. Then it will progress to more complex system design and architecture, before leading to senior positions and management. Eventual potential careers include data architect or cloud engineer.

The data scientist career path would also typically begin with entry-level positions like a data analyst, before becoming a fully-fledged data scientist. The next step would be to advance to senior data scientist roles with more responsibility and management experience. Specialised fields like AI or analytics leadership will then be open to them.

When comparing a data engineering vs data science salary, the average in the UK for the former is £56,750 and  £59,000 for the latter. This puts them fairly close together, meaning the most important thing to factor in is which career path you have the aptitude for.

How to choose between becoming a data engineer and a data scientist

Assess your individual strengths and career goals to see which camp you fit into. If building and maintaining data infrastructure appeals to you, as well as data pipelines and system architecture, then data engineering could be your future career. For those who enjoy analysing data, building predictive models and extracting insights from complex datasets, then it’s data science.