Back to all articles

14 January 2024 - 7 minutes

What Is The Difference Between a Data Engineer, a Data Scientist and a Data Analyst?

Time to split hairs! What's the difference between these three data professionals, and which one is the job for you?

Juliette Carreiro - Tech Writer

Data Science & Machine Learning

​​There isn’t a business out there that doesn’t rely on data in some way. At least no successful business doesn’t rely on data! They may handle it with an in-house team or they might outsource it to a data or consultancy agency but either way, data engineers, data scientists, and data analysts are the secret superheroes behind the world’s most powerful and impactful tech companies. But what do they do exactly?

Let’s start by looking at what data means to a business, because (spoiler alert!) it’s not just spreadsheets and targets. Data flows through a business like a stream through a forest, nourishing everything it touches–that’s the goal, at least. 

Basically, data is meant to do a lot of different things:

  • Assist leadership in making business decisions

  • Provide key insights for marketing teams, such as customer profiles and channel metrics

  • Measure the success of the business and provide insight on new avenues for expansion

  • Track employee performance on a team and individual level

  • Help product managers and product marketing managers launch new products

  • Help designers test their user interfaces

In reality, this barely scratches the surface of data’s vital role in business. Research shows that data-driven businesses enjoy more success than their data-blind counterparts, in a number of different ways.

That’s why there’s never been a better time to start a career in data and the demand for data professionals such as data analysts, data scientists, and data engineers is growing rapidly. But you’re probably wondering what the difference is between these roles and how the skills needed to land these jobs differ.

The Differences Between a Data Engineer, a Data Scientist, and a Data Analyst

It is quite common to get confused between data science and data analytics–and to be unsure of how they both relate to data engineering. Naturally, there are some fundamental differences between these three positions:

  • A data analyst needs to process and interpret data.

  • A data scientist needs to be able to build and develop tools that process information.

  • A data engineer needs to be able to build programs or systems that can take data and turn it into insights. 

With this helpful summary, let's dive a bit deeper into each.

What Does a Data Analyst Do?

To put it simply, a data analyst’s job is to take data and make it easy to understand. They take what looks like an incomprehensible jumble of numbers and transform them into actionable insights, allowing teams to make decisions. 

Knowing which landing page of a website performs best in terms of SEO, for example, or knowing how many users leave a web page immediately after visiting can be very helpful in deciding the next steps for a content marketing strategy.

The skills needed for this data analysts include:

  • Statistical knowledge and numerical skills

  • Technical skills, including Python and SQL

  • Knowledge of popular data tools, like Tableau and Heap

  • Machine learning

  • Reporting and data visualization

  • Cross-functional communication

When it comes to responsibilities, a data analyst is responsible for gathering data and compiling it in various reports, which may be based on factors such as time-frame and activity. They are also responsible for interpreting this information and then translating it into logical, meaningful, and applicable reports for their coworkers. If they’re in a startup, they may also be responsible for the internal data infrastructure of the organization, though in an ideal world this is the responsibility of a data engineer.

This role is a great starting point for anyone who is interested in a data career and it’s often the first step taken when breaking into the field. Even if your ambitions are to take your data career far, a data analyst role gives you a great foundation.

What Does a Data Engineer Do?

A data engineer develops and maintains data architecture. They are specialists in preparing large datasets so that they can be used by analysts. Where an analyst needs to interpret information, an engineer needs to build programs that can generate data into a meaningful layout. If data is a tool, think of a data engineer as a person who looks after the factory, making sure that all of the tools are nicely organized and everyone can find exactly what they need.

Keeping an entire organization’s data in check is no small feat. Therefore, it requires certain skills:

Working with both structured and unstructured data is a key component for someone in this role. Therefore, expertise in SQL is a key skill required. Tasks such as data deduplication, data management, and data cleaning are also important for a data engineer. Anyone in this role needs strong programming skills and knowledge of algorithms; building an API may be among their responsibilities, for instance, or developing a cloud infrastructure. It is a very technical function, and good knowledge of engineering and testing tools is also required.

A data engineer is responsible for developing and maintaining data pipelines. They generally need to develop processes around data modeling and data generation, which requires creative thinking and a strong problem-solving ability. Applying standard practices in data management to the needs of the business is also a crucial responsibility.

What Does a Data Scientist Do?

This role performs advanced statistical analyses and studies large datasets. They need in-depth knowledge of machine learning and data conditioning, it’s true. But you may be surprised to learn that according to real data scientists, communication is actually the most important part of the job.

So what does a data scientist’s skill set look like?

  • Statistical analysis

  • Data wrangling

  • Programming languages (SQL, Python, Ruby)

  • Deep Learning and Machine Learning

  • Communication and data visualization

  • Big Data

A data scientist is responsible for tasks such as data transformation and cleaning, working to identify and categorize various patterns in data as well as to develop machine learning algorithms to be more accurate and efficient. They need to understand the needs of the company or team they are working with in order to transform and order large datasets as required.

Data Training and Education

A career in data, whether as an analyst, scientist, or engineer, is always a great idea and a safe bet for anyone interested in the discipline. That’s because businesses run on data and that’s not something that’s going to change any time soon.

Data is not just its own discipline, but a core skill for countless different professions, like marketers, designers, consultants, and more. Basically if you walk into any office, you can bet every single person in there needs data skills! The tech industry is crying out for a constant and fresh supply of talented data professionals. Could that be you?

With Ironhack’s Data Analytics Bootcamp, you’ll get all of the skills you need to build a career in whichever branch of data most excites you. You’ll get hands-on training which will give you the experience you need to ace your first data interviews; you’ll have a dedicated Outcomes Manager and Career Services to make sure your transition to your new career goes as smoothly as possible. Forget the messy middle of job hunting!

Check out our Data Analytics bootcamp for more information (and hopefully inspiration too!)

Related Articles

Ready to join?

More than 10,000 career changers and entrepreneurs launched their careers in the tech industry with Ironhack's bootcamps. Start your new career journey, and join the tech revolution!