What’s the difference between a data scientist and a data engineer?
Quick access to the data-driven insights keeps the world’s best companies at the top of their game. But that data doesn’t just appear. Behind every predictive dashboard or crisp chart are people who collect, clean, move, and interpret the data. That’s where data engineers and data scientists come in.
While these roles do work in tandem, they solve different problems. One builds the systems that structure and move the data. The other turns that data into models and insights that drive real-time decisions.
If you’re considering a career in data or hiring for one, knowing the difference between data engineers and data scientists can steer you in the right direction. Here’s how they work, where they intersect, and what it means for your future.
Key points
- Data engineers create the pipeline. They build the infrastructure that moves and shapes raw data.
- Data scientists unlock the meaning. They take structured data and use modeling, machine learning, and analysis to surface insights that drive business strategy.
- Each role uses distinct tools. Engineers go deep on SQL, Spark, Kafka, and cloud platforms. Scientists lean on Python, R, TensorFlow, and Tableau for exploration and modeling.
- Their collaboration drives progress. Engineers prep the data, and scientists make sense of it.
- Choosing a path depends on your mindset and interests. If you like building systems, consider engineering. If you like solving puzzles with data, consider science.
What does a data engineer do?
Data engineers design and maintain the systems that move raw data from scattered sources into centralized warehouses or lakes. Think of them as builders. They create the architecture that powers everything from machine learning models to business dashboards. Their work gives data scientists, analysts, and decision-makers access to reliable data at scale.
A data engineer’s responsibilities often include:
- Building and maintaining ETL (extract, transform, load) pipelines, which pull raw data from sources, clean it up, and move it into storage systems
- Managing data warehouses and real-time data streams
- Automating workflows and ensuring data integrity
Data engineers’ toolboxes often include languages like SQL and Python, frameworks like Apache Spark and Kafka, and cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure.
Ultimately, becoming a data engineer means creating and owning data pipelines—their creation, integrity, and management. Good data engineers set the stage for data scientists to mine these pipelines for the golden nuggets of insight businesses need to thrive.
What does a data scientist do?
Once prepped, data scientists dig into large datasets. They look for trends, build models, and run experiments. While data engineers focus on structure, data scientists focus on meaning.
Data scientists’ day-to-day duties often include:
- Putting the final touches on datasets for analysis
- Creating statistical models and machine learning algorithms
- Translating findings into reports, dashboards, or product recommendations
They rely on tools like Python and R for data wrangling, Jupyter Notebooks for exploration, and TensorFlow or PyTorch for machine learning. Visualization platforms like Tableau or Power BI help them turn results into stories that teams can understand and implement.
But data scientists’ insights are only as good as the data foundation beneath them—the data built and maintained by engineers. Scientists and engineers working together form the backbone of any data-driven team.
Data scientists vs. data engineers: Similarities and differences
Both roles work with data, write code, and solve problems, but what they solve and how they solve it is where things split.
Here’s a quick side-by-side look at what sets data scientists and data engineers apart, and how they work together:
| Category | Data engineer | Data scientist |
| Core responsibilities | Builds and maintains data pipelines and infrastructure | Analyzes data, builds models, and finds insights |
| Tools and tech | SQL, Spark, Kafka, Airflow, cloud platforms | Python, R, TensorFlow, Tableau |
| Collaboration | Delivers clean, structured data to teams | Uses that data to create forecasts and recommendations |
| Education and skills | Background in computer science or software engineering | Background in math, stats, or data science |
| Career outlook | High demand in infrastructure, cloud, SaaS | High demand in finance and health care |
Let’s break these down one by one.
Core responsibilities
Data engineers build the systems that move and manage data. They focus on the architecture—pipelines, warehouses, APIs—and make sure everything flows smoothly and accurately.
Data scientists take an engineer’s work and use structured data to build models and generate insights that drive strategy.
Tools and technologies
Data engineers use tools to move and process large volumes of data. These tools include SQL for querying, Spark for big data, Kafka for streaming, and Airflow for automation. Most work in cloud environments like AWS, GCP, or Azure.
Data scientists focus on exploration and modeling. Python and R are the go-to languages, with libraries like pandas, scikit-learn, and TensorFlow. For sharing insights, they use tools like Tableau, Power BI, and notebooks like Jupyter.
Collaboration in the data pipeline
Data engineers and data scientists are two pieces of the same puzzle. Engineers deliver structured, reliable data that is clean enough to analyze and rich enough to get results. Scientists use that data to test hypotheses, build models, and tell stories that influence product decisions.
Take fraud detection, for example. A data engineer might build the pipeline that pulls transaction data from multiple systems in real time, cleans it, and routes it to a centralized database. The data scientist uses that stream to train a model that flags suspicious behavior based on patterns. When fraud trends shift, the scientist updates the model, and the engineer adjusts the pipeline to add new signals.
Skills and educational background
Becoming a data engineer is a popular engineering career path for those with computer science or software engineering backgrounds. They’re strong in coding and database design. Certifications in cloud platforms or tools like Spark can also boost their profile.
Becoming a data scientist often involves studying math, statistics, or data science itself. They combine analytical thinking with storytelling skills and fluency in modeling, machine learning, and visualization.
Both roles benefit from soft skills like collaboration, curiosity, and clear communication, because building or interpreting data is only half the job. Sharing it clearly with others is the rest.
Career outlook and job opportunities
Demand for both roles is strong and growing. Companies hire data scientists to predict customer behavior, forecast trends, and personalize experiences.
According to the US Bureau of Labor Statistics (BLS), data scientists command a median annual salary of $112,590. The median for data engineers comes in at about $130,000 per year.
In addition to a six-figure salary, the BLS projects that the broader data science category of jobs will experience 36% growth through 2033.
Which role is right for you?
Succeeding as a data engineer vs. a data scientist depends on how you think and work. If you love building systems and making data reliable at scale, you might be wired like a data engineer. If you’re inquisitive and excel at telling stories with numbers, a data scientist path might fit you better.
Still curious? Try out projects on both sides. Learn SQL. Play with Python. See what sticks. And if you’re ready to take the next step, explore data science jobs at Intuit.