In this article, we discuss the differences when it comes to data engineer vs data scientist vs data analyst as well as explaining what kinds of professional context each role is most suited for.
Data engineer, data scientist and data analyst are all job titles that sound like they could be describing similar positions. But there are actually significant differences between each of the roles that are important to understand whether you’re an employer whose business handles data of any kind or an employee wondering what the roles and responsibilities are for each position.
Data engineering prepares data for analytical use. It’s a variation of software engineering that focuses on the design and development of information systems that store and handle large sets of data.
Without data engineering, raw data would arrive with analysts in a totally incomprehensible format, requiring much more time and effort to organise before any information could be taken from it. Data engineering creates solutions for streamlining and organising data so that it can be handled easily, improving the efficiency of countless systems and processes.
A data engineer is an experienced data handler who is responsible for designing, building and maintaining digital architecture for data. They create the foundation of a variety of data operations, extracting, transforming, ordering and storing information so that it can be easily accessed and used by others.
Data engineers often have to create systems that take data from a range of different sources and then compile this so that it is all in the same format, without any errors, in a way that can be understood by data analysts. A background in software design or programming is required for this role as well as experience collating and handling data, which is why a lot of data engineers began their careers doing data analysis.
Typical responsibilities of a data engineer revolve around creating new frameworks and databases for new sets of data which includes developing data pipelines, creating processes for modelling and extraction, testing and refining architecture they have built, liaising with data analysts and scientists to understand project requirements and updating and maintaining systems to ensure continued accuracy.
Skills required for successful data engineering include competency in a range of programming languages, experience with database systems, software and data architecture abilities, problem-solving and collaboration.
Data science is an interdisciplinary field of study that uses scientific theory to gain insights from sets of data. Scientific methods of research are combined with classic data analysis, statistics and systems of artificial intelligence to analyse and draw conclusions from a wide range of data and then apply these findings to a variety of fields.
It’s only in recent years that data science has emerged as a discipline because it specifically focuses on digital data and using digital processes to extract meaning and insight from this information. Despite having ‘science’ in the name, data science is more concerned with business and developing new ways to facilitate business growth and success.
A data scientist doesn’t tend to have any actual scientific training, and instead is a very experienced analyst who uses machine learning, mathematical modelling, and business insight to look at data, draw conclusions and identify trends that can be capitalised on. It’s a relatively new role that has come about because of the huge explosion in data that there has been over the last decade, focusing on making the most out of all this information we now have access to.
Data scientists work in a range of industries and are usually involved with business development and management. They use digital tools and programming skills to create new ways of handling and manipulating data so that new insight can be gained, processes can be streamlined and solutions can be developed.
Typical responsibilities of a data scientist include applying scientific and mathematical methodology to data to find solutions to business problems, developing algorithms for data extraction, creating data and operational models, offering insight into strategic planning and making suggestions for future progress.
Successful data scientists need to have strong statistical analysis, problem-solving and lateral thinking skills, as well as knowledge of both mathematical and statistical theory and business development. Data scientists have to liaise with a wide variety of people in their role, so excellent interpersonal and communication skills are also required.
Data analysis is the process of interpreting information taken from a collection of data. It can involve gathering and organising the data as well as using extraction, modelling and visualisation techniques to gain the necessary insight.
There are a wide variety of industries that use data analysis as a way of generating reports, measuring progress and growth, identifying trends and understanding behaviours or patterns. Data-driven decisions tend to be more successful than those that are made without any context, making data analysis a vital part of positive growth.
A data analysis role is usually the first rung on the ladder in the career of someone who wants to start working with data. Whilst it doesn’t require any specific training, those in the role will benefit from strong mathematical abilities and the ability to quickly generate and understand statistics.
The typical responsibilities of a data analyst include collecting and organising data in response to requests from clients or their employer, collaborating with others in the team to devise methods of data collection, pre-processing data to remove errors, interpreting and drawing conclusions from data sets, creating reports or presentations to summarise their findings and identifying the best ways to visualise data and their conclusions.
Skills needed for a role in data analysis include analytical thinking, efficient trend and pattern identification, statistical knowledge, data visualisation, confident presentation abilities, independent working and self-motivation. Whilst data analysis might be done as a section of wider report production or presentation preparation, most data analysts are responsible for entire sets of data and work independently to order, analyse and interpret this.
Data engineers, data scientists and data analysts all often work together on projects that involve large sets of data, and the majority of people in these positions will have similar backgrounds when it comes to education and experience. However, several significant differences differentiate the roles and impact who can do each and when each is required.
Firstly, the levels of responsibility between the roles differ significantly. A data analyst is the most junior role and whilst they are in charge of managing data sets and extracting information from them, they tend to work under more experienced analysts. Data engineers are responsible for creating the framework that sets of data will be stored in and extracted from, and they use their knowledge and programming experience to come up with unique solutions each time. A data scientist often works in a kind of consultancy role, taking the work of both the data analyst and engineering and using this to draw their own conclusions and use data in more complex models and systems.
Next, a data analyst needs mathematical thinking skills and a good head for data, but doesn’t require any specific prior training or background in a subject to do well at their job. A data engineer however needs either computer engineering, programming or software development experience in order to create databases and pipelines for data to be transferred to, and a data scientist needs an understanding of a variety of mathematical, statistical and business theories as well as the skills to create data analysis models and systems.
Both data analysts and data engineers are not actively involved in any decision making for the company they work for, whereas a data scientist has a significant impact and often leads the direction that a business goes in.
When it comes to smaller aspects of each role, a data analyst only deals with structured data, whereas engineers and scientists also deal with unstructured data. Data analysts and scientists both need to be experienced in data visualisation to do well in their roles, whilst a data engineer does not. Finally, data engineers and scientists both need experience programming and creating models or systems that data can be stored and manipulated in, whereas a data analyst role does not require these skills.
A data engineer is required when a business needs a new framework, database or system creation that is going to take raw data and organise it into information that can be manipulated and analysed. The scale of this kind of project will vary depending on the kind of data being stored and the number of sources it is coming from, so the engineer may be hired permanently or just come in as a contractor at the start of a new project.
Data engineers often begin their careers as software developers, web developers or data analysts with programming experience. Recruiting a data engineer can be done through a variety of classic routes such as advertising the role or working with a specialist recruitment agency, or you may be able to source an engineer from your existing pool of analysts.
The role of a data scientist is in incredibly high demand at the moment because of how rapidly technology is developing which can take all kinds of data and make predictions, identify trends and provide solutions to business problems that otherwise would have been left unsolved. Whether you’re a company that deals with large amounts of data and are looking for an employee or consultant who can improve your offering, or are looking to grow your business through data-driven solutions, a data scientist is a person who can do just that.
Data scientists are very in demand at the moment, so if you’re looking to hire one you’ll have to do your research into current professionals in the industry and identify those who you feel would be a good fit for your company. Valuable data scientists have a background in data engineering and analysis as well as having a head for business and knowledge of the industry they work in, so if you find a candidate that fits the criteria, don’t let them go!
A data analyst is required in situations where sets of data need ordering and information or insight needs extracting from them. All kinds of industries use data analysis to understand behaviour, assess progress, make predictions and develop new products or services, but businesses that provide data handling or management services in particular will have more use for the role.
Data analysis does require critical thinking skills, quantitative and qualitative analysis and a good understanding of the area you are working in, but there aren’t any specific areas of study or experience needed to begin a job in the role. Employers looking to hire data analysts will have success finding potential candidates through graduate recruitment schemes or in existing positions involving data handling and general analysis.
Data engineers, scientists and analysts may often all be part of the same team and overlap in some of their skills or responsibilities, but the roles themselves have very different backgrounds and require different levels of skill and experience. Understanding the difference between them is vital whether you’re an employer or an employee, as each is required in a different scenario and each role overlaps with different areas of expertise that inform the work that gets done.
If you’re an employer in the embedded systems industry that is looking for specialist help with hiring data engineers, analysts or scientists, get in touch and find out more about how our recruitment agency can help you.
Chris is an award-winning recruitment consultant who has specialised in the electronics and embedded systems sector since 2008. Chris is passionate about technology and customer service.
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