Data Analyst, Data Scientist, Data Engineer — what’s the difference between all of the jobs in data?

Working in data is a popular career path at the moment — companies are continuing to collect more and more data than ever, and understand the need to use that data to inform their business decisions.

But what’s the difference between all of the different roles? And which job should you target if you’re trying to break into the data industry?

Common titles for jobs in data include Data Analyst, Data Scientist, Data Engineer, Business Intelligence Analyst (or Engineer), Machine Learning Scientist, Analytics Engineer, Data Product Manager, Business Analyst … and more variations.

Unfortunately, there is no clear-cut definition for each role. Job titles are vague and inconsistent across — and sometimes even within — companies. It’s always important to read job descriptions and ask questions during interviews to figure out what a role will actually focus on.

As for what job to target for your career, that’s going to depend on what you enjoy and how much time and effort you’re willing to put into learning.

Below are some general job descriptions, however, these are not hard and fast rules. There is no governing body that approves job titles. It truly is up to the company — specifically the hiring manager and the HR team.

Note that I haven’t worked in all of these roles so I can’t speak to the specifics of all of them. This is just meant to give a very high-level idea. You can discern a lot more once you start reading job descriptions — or by networking with folks in these roles!

Data Analyst

What they do: Uses data to report on what has happened via ad hoc analysis or dashboards.

Presentations & Meetings? Often has to meet with stakeholders to understand their needs as well as present their insights to stakeholders and leadership.

Skills: Excel, SQL, Tableau or Power BI, and sometimes Python or R. Knowledge of basic statistics (descriptive statistics for basic roles, and statistical tests for more advanced roles).

Education: Usually a minimum of a bachelor's degree is required. Statistics and computer science are the closest subjects, but there are folks working as Data Analysts with all types of majors, including non-STEM.

Data Scientist

What they do: Uses data to predict what could happen in the future through predictive modeling and/or hypothesis testing. But also sometimes reports on what has happened in the past through exploratory data analysis. Could also research and build machine learning models to automate things.

Presentations & Meetings: Often has to meet with stakeholders to understand their needs as well as present their work to stakeholders and leadership.

Skills: SQL, Python and/or R. Usually requires knowledge of statistics, probability, and maybe also linear algebra and calculus. Often requires familiarity with predictive models/machine learning algorithms and how to evaluate them.

Education: Often requires or prefers an advanced degree (master’s or Ph.D.) in a STEM or research-based field, but some folks only have bachelor's degrees. Statistics and computer science are the recommended subjects, but it is common to see Data Scientists with other quantitative degrees like mathematics, engineering, and physics. Any research-based STEM degree is also good. Some folks have been able to pivot into Data Science roles with non-STEM degrees as well.

Data Engineer

What they do: Builds data pipelines to store and transform the data used by analysts, scientists, or for dashboards.

Presentations & Meetings: Has to meet with stakeholders and provide demos of work, but usually not as many presentations as Data Analysts or Scientists.

Skills: SQL, Python, maybe other programming languages, and cloud-based tools (like AWS, Snowflake, etc).

Education: Usually requires a bachelor's degree in computer science or equivalent skills.

Analytics Engineer

Newer role that is described as a hybrid between Data Analyst and Data Engineer. Expect any and all of what is listed under those roles.

Machine Learning Scientist

What they do: Researches and builds production-worthy machine learning models to automate things.

Presentations & Meetings? Occasionally or often has to meet with stakeholders to understand their needs as well as present or demo their work to stakeholders and leadership.

Skills: SQL, Python and/or R. Needs very good coding skills. Requires knowledge of statistics, probability, linear algebra, and calculus.

Education: Often requires or prefers an advanced degree (master’s or Ph.D.) in a STEM or research-based field. Statistics and computer science are the recommended subjects, but it is common to see ML Scientists with other quantitative degrees like mathematics, engineering, and physics. Any research-based STEM degree is also good.

Machine Learning Engineer

What they do: Takes the models built by the Machine Learning Scientist and productionalizes them. More of a specialized software engineer role.

Presentations & Meetings? Occasionally has to meet with stakeholders (ML Scientists) to understand their needs, or present to stakeholders or leadership, but probably not as often as ML Scientists.

Skills: SQL, Python, maybe other programming languages, and cloud-based tools (like AWS, Snowflake, etc).

Education: Usually requires a bachelor’s degree in computer science or equivalent skills.

Business Intelligence Analyst or Engineer

What they do: Builds dashboards, and often the supporting data tables.

Presentations & Meetings? Occasionally has to meet with stakeholders to understand their needs as well as present or demo their work to stakeholders and leadership.

Skills: SQL, Tableau and/or Power BI, sometimes Python.

Education: Usually requires a bachelor's degree, computer science is a common subject.

Data Product Manager

What they do: A product management role overseeing data collection, transformation, storage, and governance. Usually not a hands-on data role, but more of a specialized project manager and subject matter expert.

Presentations & Meetings? Frequent meetings and presentations.

Skills: Usually requires more business knowledge than technical skills.

Education: Usually requires a bachelor's degree. Major isn’t as relevant since there aren’t as many technical skills required.

Business Analyst

What they do: Solve problems for the business through understanding of data and non-quantitative information.

Presentations & Meetings? Frequent meetings and presentations.

Skills: Usually requires more business knowledge than technical skills, but not uncommon to use Excel, SQL, Tableau or Power BI.

Education: Usually requires a bachelor’s degree. Major isn’t as relevant since there aren’t as many technical skills required.

Do you disagree with any of the above descriptions or have more to add? Let me know in the comments!

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