What is the difference between Data Science and Data Engineering?
A data scientist without a data engineer is like… well, I guess a chef without a kitchen.
You can be a genius in your field, developing complex recipes and delicious dishes. But if food deliveries are haphazard, the fridge constantly breaks, and half the ingredients disappear en route, the restaurant will have to close.
Data teams are structured similarly.
Data engineers build the ‘kitchen’: they collect data, set up pipelines, warehouses, and infrastructure.
Data scientists use it: they analyze data, look for patterns, build models, and help businesses make decisions.
And the demand for data specialists is clearly not decreasing.
According to the U.S. Bureau of Labor Statistics, employment of data scientists in the US is expected to grow by 33.5% between 2024 and 2034 – significantly faster than the average employment rate for all occupations.
So, if a company is choosing between hiring a Data Engineer or a Data Scientist first, the answer depends less on the job title and more on what’s currently not working in your data.
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Data engineering vs data science: A brief comparison
Data science engineering sits between data science and software engineering. In simple terms, you take an ML model and make it work in the real world. Kind of magic outside of Hogwarts.
A data scientist builds the model. A data science engineer gets it into prod and makes sure it can handle real data, users, and traffic.
Basically, finding such rare diamonds (I mean, these specialists) is kinda challenging.
Which is why leveraging professional expertise from targeted recruiting services is often the most efficient way for modern companies.
Data engineer vs data scientist vs data analyst
Mapping out what the best of the best do at their workplace:
Data engineers build the plumbing. They set up data pipelines, manage warehouses and lakes, and make sure data gets where it needs to go without breaking along the way.
Data analysts work with the data: they use SQL, BI tools, and pandas to crunch the numbers, track KPIs, run A/B tests and answer “SO WHAAAAT?” questions behind every business decision.
Data scientists go a step further. They use statistics and ML to spot patterns, predict what might happen next, and build models to automate more complex decisions.
If you want to understand how these concepts overlap and differ within the wider technology landscape, our comprehensive comparison hub, Data Science vs Machine Learning vs AI, provides an ideal starting point.
Why should we really bother with a Data Engineer?
Data Engineering is a part of the IT infrastructure that no one notices… until it breaks. However, as an ML Engineer at an AI service company, I bet: as soon as the pipeline falls, the entire business panics.
You can explore best practices and methodologies in our detailed guide about data collection.
Consider a typical retail or e-commerce business. Today, terabytes of data are generated there: site logs, cash register transactions, CRM system data, advertising account data, and courier geolocations. Everything exists in different formats and places. Well, how would you react?
But a Data Engineer would take away this chaos, clearing it all, connecting it with each other, and storing it in a single reliable storage (Data Warehouse or Data Lake), using star or snowflake schemas where it’s necessary.
To dive deeper into this topic, check out our insights on ML infrastructure key components and challenges.
What does a DE do:
Pipelines (ETL/ELT): Airflow, DAGs, dbt, Prefect.
Data storage and streaming: Snowflake, BigQuery, PostgreSQL, Apache Kafka, Spark, Hadoop.
All infrastructure: AWS, GCP, Azure, Docker, Kubernetes, API integration, data governance.
Core DE principle: scalability and reliability. Writing a script that will transfer 100 GB of data is a simple task. Work in such a way that this script does not cost the company millions of dollars; if you have 100 terabytes, everything builds on proper engineering.
Why should we really bother with a Data Scientist?
Once the Data Engineer has laid the foundation and cleaned up the data, the Data Scientist steps in. His task is to put this data to work and find ways to drive revenue for the business.
If an engineer asks, “How can we quickly deliver data from point A to point B?”, then a data scientist asks, “How will these 10,000 clients sign up for a subscription this month?” or “What price should I put on the hotel in order to maximize profits?”
What DS does:
Analysis and statistics: Python (pandas, numpy), Jupyter Notebooks, BI tools, understanding of divisions, A/B tests, hypotheses, regression analysis.
Machine Learning: feature engineering, hyperparameter tuning, scikit-learn, XGBoost, CatBoost, classification models.
Deep Learning & GenAI: PyTorch, TensorFlow, neural network, Hugging Face, LLM API, RAG.
In practice, a Data Scientist spends most of their time not on “training beautiful neural networks” but on pre-investigation analysis (EDA), formulating business hypotheses, and collaborating with product managers.
Education and background
Data engineering candidates usually hold degrees in Computer Science, Software Engineering or IS, focusing on algorithms, database management, system architecture.
Data science professionals emerge from a background in Data Science (call me Captain Obvious), statistics, mathematics or physics, leaning into probability and econometric modelling.
Who should a business hire in a company?
This is a huge disappointment for the rich founders of startups: they buy the AI apps, hire a strong Data Scientist, and then it turns out that they have nothing to do with it.
You will immediately need a Data Engineer because:
The data lies in three different places, the folders in the Excel spreadsheets do not work with the CRM, and the pipelines fall apart.
Analysts spend 80% of their time simply extracting and manually cleaning up valuable information.
You want to get rid of ML, but you don’t have a stable collection of data.
(If you hire a Data Scientist in such chaos, you won’t solve your problems. You’ll just end up with an expensive bottleneck with greater frustration…)
You need a Data Scientist if:
You already have a functioning system, and the data is regular, clean, and structured.
And specific business tasks for optimization: the need to change the flow of clients, create a recommendation system, or develop a model to forecast demand.
For companies that need specialized expertise without having to immediately build a large internal team, our data engineering services or data science outsourcing will be of great help.
Well, that’s just a reminder.
Time is your most valuable asset, and we’re not gonna waste it!
Our experts work with hundreds of clients around the world and know the drill. Want to integrate AI with maximum value?
How data engineers and data scientists work together
Sometimes you need both.
This is especially true when a company transitions from “we have a lot of data” to “we want to build data-driven products”.
A data engineer creates the foundation.
A data scientist turns that foundation into models and insights.
The product team turns those insights into products that real people use.
Our company has already had so many different proofs here.
Data Scientist or Data Engineer: Which career path is right for you?
Don’t be surprised by the salary gap or media popularity (they’re paying off big money and making a big splash). Think about what gives you more drive:
Select Data Engineering if you need system programming, digging into architecture, optimizing databases, complex mechanisms, and learning how a great system works like a year without your participation.
Choose Data Science, as you are a researcher by nature: you like to twist data from different sides, find connections, and joke about business ideas through mathematics, and there will be intelligent algorithms.
The main thing is that there is a strong connection between roles. Specialists who are able to find their niche (for example, ML Engineers, and the transition from data scientist to data engineer or vice versa) are the most valuable professionals currently.
It’s better to choose the type of tasks you really want to tackle for the next few years. I know what I’m talking about…
My last but not least thoughts
Data Engineering and Data Science aren’t two competing versions of the same profession.
They solve different parts of the same problem.
Data Engineering makes data reliable, accessible, and scalable. Data Science turns that data into insights, predictions, and models.
And strong data teams need both.
If you’re a business deciding who to hire, first identify the main bottleneck.
If you’re choosing a career path, consider the problems you want to solve, not just which job title sounds more prestigious.
Because ultimately, the question isn’t:
“Data Engineer or Data Scientist?”
But:
“What needs to be done with our data to make it useful?”
And the answer to this question will tell you where to start.
If you’re unsure what data strategy, architecture, or team structure your project requires, data science consulting can help you determine the optimal approach.
FAQ
What's the difference between a Data Engineer and a Data Scientist?
A difference between data science and data engineering is that a Data Engineer creates the infrastructure and pipelines that make data reliable and accessible.
A Data Scientist analyzes this data to gain insights, conduct experiments, and build forecasts and ML models.
Simply put:
A Data Engineer creates a data foundation. A Data Scientist uses it to solve analytical and predictive problems.
Is Data Engineering more difficult than Data Science?
Objectively, no.
They are simply different types of complexity.
Data Engineering can involve distributed systems, databases, cloud infrastructure, scalability, reliability, and complex production environments.
Data Science involves statistics, machine learning, experiments, ambiguous business problems, and the need to work with uncertainty.
Therefore, the field whose tasks are less intuitive to you usually seems more complex.
Who earns more: a Data Engineer or a Data Scientist?
There is no definitive answer if you want to examine a data science vs data engineering salary.
Salaries depend on the specialist’s level, country, industry, specialization, company, and many other factors. Moreover, government statistics don’t always allow for a direct comparison of these two professions.
For example, the BLS lists the median salary for data scientists at $120,230, while for database architects, a broader category that may partially overlap with data engineering, the median salary was $144,440.
Therefore, it’s better to compare specific job openings and levels, not just job titles.
Will AI replace data engineers and data scientists?
AI is already changing both professions, but this is not the same as completely replacing them.
AI can automate some tasks related to data preparation, SQL generation, exploratory analysis, coding, documentation, and model development.
But companies still need specialists who understand:
what data needs to be collected;
whether it can be trusted;
how the system should be built;
what business problem is truly worth solving;
whether the model output is useful;
how to properly evaluate quality;
How to safely deploy a system into production.
Moreover, the BLS predicts strong growth in demand for data scientists by 2034, by 33.5%. This growth is partly due to the development of AI and the increasing volumes of data available for analysis.
