Data Engineer vs Data Scientist vs Data Analyst: What Each One Does
These three titles get used interchangeably by people hiring for them, which is why so many data teams end up with the wrong first hire. They are not seniority levels of the same job. They are three different jobs that depend on each other, and a team that hires them in the wrong order usually discovers it about six months in.
This page sets out what each role does day to day, what separates them in practice rather than in job titles, and how to work out which one a particular team needs first.
What does a data engineer do?
Builds and maintains the systems that move data from where it is created to where it can be used, and keeps it reliable.
A data engineer works on pipelines, storage and the plumbing between systems. They take data out of source systems, transform it into something consistent, and land it somewhere the rest of the business can query. They own whether that happens on schedule, whether it is correct, and what happens when a source system changes shape without warning.
The work is closer to software engineering than to analysis. It involves writing and maintaining code, thinking about failure, and being responsible for something that runs whether or not anyone is watching. A useful test: if the question is “why is this table empty this morning”, it is a data engineering question.
What does a data scientist do?
Builds models that estimate or predict something the business cannot observe directly.
A data scientist works on questions that need statistical inference rather than counting. Which customers are likely to leave. What this claim will probably cost. Which of these two changes caused the difference. The output is usually a model, an estimate with uncertainty attached, or an experiment design, and the value is in the judgement about method rather than in the code.
The distinguishing question is whether the answer exists in the data already. If it does and needs finding, that is analysis. If it has to be estimated from patterns, that is data science.
What does a data analyst do?
Answers questions the data already contains, and makes the answer usable by someone who will act on it.
An analyst works with data that exists, in service of a decision someone is about to make. Why did revenue fall in this region. Which segment is driving the change. What does the funnel look like this quarter against last. The skill is partly technical and substantially about understanding the business question well enough to answer the one that was meant rather than the one that was asked.
Analysts are frequently the most underrated hire of the three, because their output looks simple when it is done well. A good analyst prevents more bad decisions than a model usually does.
How do the three roles actually differ?
By what they own, what they produce, and what breaks when they are absent.
Data engineer | Data scientist | Data analyst | |
Owns | Pipelines, storage, reliability | Models and method | Questions and reporting |
Produces | Systems that run | Estimates and predictions | Answers and dashboards |
Core skill | Software engineering | Statistics and inference | Business judgement and SQL |
Typical question | Why did this pipeline fail | What will happen | What happened and why |
If absent | Nothing is trustworthy | No forward looking view | Nobody uses any of it |
The last row is the one that decides hiring order. Without an engineer, the other two spend most of their time cleaning data by hand and the organisation pays senior salaries for manual work. That is the most common and most expensive sequencing mistake in building a data team.
Which role should a team hire first?
Usually the engineer, unless the data is already reliable and accessible, in which case an analyst.
Ask one question: can somebody get the data they need today without asking a developer for help. If the answer is no, hire the engineer. Bringing in a data scientist ahead of that produces a well paid specialist spending most of the week on extraction, which is both a waste and a reliable way to lose them inside a year.
If the data is already accessible and trustworthy, hire the analyst next. Most organisations get more value from consistently good answers to ordinary questions than from a predictive model nobody has the pipeline to deploy. The data scientist is the third hire far more often than the market chatter suggests, and teams that take that order tend to keep the person they eventually recruit.
Which path should someone entering the field choose?
Follow what you would rather be responsible for when something goes wrong at seven in the morning.
Students and career changers usually ask this as a question about which field pays better or is more secure, and the honest answer is that the difference between them is smaller than the difference between being good and average at either. The more useful question is about the nature of the work.
Data engineering suits people who like building things that keep running, are comfortable owning something operational, and get satisfaction from a system that behaves. It carries on call responsibility in many organisations. Data science suits people who like open ended questions, are comfortable with an answer that comes with error bars, and can tolerate long stretches where the finding is that the effect is not there. Analysis suits people who like being close to the decision and want to see their work used this week rather than next quarter.
Anyone genuinely unsure is usually better starting in analysis. It is the shortest route to working with real data and real stakeholders, and it is the easiest of the three to move out of in either direction once you know which part you liked.
Can one person cover more than one of these?
At small scale yes, and the combinations that work are specific.
Analyst and data scientist combine reasonably well, since both work downstream of the pipeline and share a way of thinking about questions. Engineer and analyst combine acceptably at very small scale. Engineer and data scientist is the hardest combination to hire for and the least likely to be genuinely strong at both, because the disciplines pull in different directions and few people maintain depth in each.
The related question of where the engineering discipline itself is heading is covered in our piece on the future of data engineering.
What should the job advert actually say?
Name the role by what it owns, and describe the state of the data honestly.
A large share of failed data hires trace back to an advert that described one job and a reality that was another. The most common version is a data scientist advert for work that is ninety percent pipeline building. Candidates take the role, discover it in week three, and leave inside a year, and the employer concludes that data scientists are difficult.
Two things fix most of it. Name the role for the thing it owns rather than for the title that attracts the most applications. And say plainly what state the data is in, because a candidate who knows they are joining a messy environment and signs up anyway is a candidate who will stay. The ones who feel misled do not.
Frequently asked questions
1. Is data engineering part of data science?
No. They are separate disciplines that depend on each other. Data engineering is closer to software engineering; data science is closer to applied statistics.
2. Which of the three is hardest to hire?
Data engineers, consistently, because the skill set overlaps with software engineering and the people who have it have other options that pay comparably with less on call responsibility.
3. Does a small company need all three?
Rarely at the start. Most small organisations need reliable data and good answers long before they need predictive models, which means an engineer and an analyst cover the ground for some time.
4. Do analytics engineer and machine learning engineer fit anywhere in this?
Analytics engineer sits between engineer and analyst, owning the transformation layer and the definitions everyone reports against. Machine learning engineer sits between engineer and scientist, owning models once they have to run in production rather than in a notebook. Both are real roles rather than inflated titles, and both usually make sense only once the three core functions exist.
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