In today’s digital world, data is one of your organisation’s most valuable assets. But making sense of this data, and making it work for you, requires specialised roles. Two key players in this space are data engineers and data scientists. Both are vital in turning raw information into meaningful outcomes, yet their work, skills, and daily focus are quite distinct.
Understanding these differences can help you choose the right path if you’re pursuing a career in data. For business leaders, knowing how these roles complement each other is key to building high-performance data teams.
In this guide, we explore what each role does, how to enter the field, the tools they use, and how they contribute to AI-driven innovation.
1. Data Engineer vs Data Scientist: What Do They Do?
Although they often work together, data engineers and data scientists focus on very different parts of the data journey.
What Does a Data Engineer Do?
Data engineers are responsible for building the infrastructure that stores and moves data. Think of them as the architects and builders of data highways, ensuring that information is collected, cleaned, and made available in usable formats.
Key responsibilities:
- Design and maintain data pipelines
- Build and manage data storage systems (e.g., databases, data lakes)
- Ensure data quality, integrity, and accessibility
- Collaborate with analysts and scientists to deliver the right data
What Does a Data Scientist Do?
Once the data infrastructure is in place, data scientists step in to explore, analyse, and draw insights. They apply statistical models and machine learning to solve business problems and make predictions.
Key responsibilities:
- Analyse structured and unstructured data
- Build predictive and classification models
- Create visualisations and dashboards to share insights
- Translate findings into strategic recommendations
Where They Overlap
While the roles are distinct, there's natural collaboration between them. Data scientists rely on clean, accessible data pipelines built by engineers, while engineers adjust systems based on the needs of data science.
Additionally, roles like machine learning engineers or data analysts often blend responsibilities from both areas, reflecting the increasing need for interdisciplinary data skills.
2. How to Start a Career in Data Engineering or Data Science
Whether you're planning your first job in tech or switching careers, there are flexible ways to break into data engineering or data science. Let's look at what typically sets these paths in motion.
Educational Backgrounds That Help
There’s no single required degree to become a data engineer or data scientist, but some fields naturally align with each role.
Data engineers often come from technical disciplines such as:
- Computer Science
- Software Engineering
- Information Technology
Data scientists frequently have backgrounds in:
- Statistics
- Mathematics
- Data Science or Computer Science
However, many professionals transition into data science from other domains, especially if they have strong analytical or research skills. According to an Indeed study, while 75% of data scientists hold a master’s or PhD, their degrees span a wide range of subjects, including astrophysics, zoology, software engineering, and analytics.
Certifications and Online Courses
Thanks to online learning, it’s easier than ever to gain relevant, job-ready skills.
Popular platforms and programmes include:
- Coursera, edX, Udacity, and DataCamp
- Google Cloud and AWS certifications
- Specialised bootcamps for data engineering or data science
Key focus areas:
- Engineers: Big data tools (Hadoop, Spark), cloud platforms, databases, ETL processes
- Scientists: Machine learning, data visualisation, Python libraries (Pandas, Scikit-learn)
Entry-Level Job Titles
Here are common starting roles that help you break into the industry:
| Career Track | Entry-Level Roles |
| Data Engineering | Junior Data Engineer, ETL Developer, Database Admin |
| Data Science | Data Analyst, Junior Data Scientist |
3. Core Tools and Skills: What Sets Data Engineers and Data Scientists Apart
While both roles work with data, their tech stacks and daily toolkits reflect different focus areas.
Programming Languages
Both roles use programming, but with different emphasis:
| Role | Common Languages Used |
| Data Engineer | Python, Java, Scala, SQL |
| Data Scientist | Python, R, SQL |
Engineers use programming to build scalable systems and automate data movement. Scientists focus on coding for analysis and modelling.
Python continues to be highly popular among developers: the 2024 Stack Overflow survey shows 51% of developers use it, making it the third most popular language overall.
Data Storage & Access
Understanding where and how data is stored is crucial for both roles, but in very different ways.
Data Engineers:
- Set up and maintain databases and data lakes
- Use platforms like PostgreSQL, MongoDB, and Hadoop
- Ensure fast, secure, and structured access
Data Scientists:
- Query and access data using SQL or APIs
- Work in Jupyter Notebooks or BI tools
- Focus on exploring, cleaning, and modelling the data
Visualisation and Reporting
Turning raw numbers into compelling stories is especially important for data scientists.
| Role | Visualisation Tools |
| Data Engineer | Grafana, Kibana (system dashboards) |
| Data Scientist | Tableau, Power BI, Seaborn, Plotly |
Engineers build monitoring tools for data health, while scientists create charts to present insights and influence business strategy.
Cloud Platforms & Pipelines
Modern data work largely happens in the cloud, and both roles rely on these ecosystems:
- Shared platforms: AWS, Azure, Google Cloud
- Engineers focus on:
- Building and automating pipelines (e.g., with Apache Airflow or AWS Glue)
- Ensuring data arrives clean and on time
- Scientists focus on:
- Using clean, structured data for analytics
- Running models and experiments in cloud-based notebooks
Developers are embracing AI coding tools too. According to a 2024 survey, 76% of engineers use or plan to use AI coding tools, up from 70% in 2023.
4. A Look at Their Daily Workflows
Understanding how these professionals spend their time gives you a clearer picture of what the job actually looks like.
What a Typical Day Looks Like for a Data Engineer
Data engineers focus on system performance, data quality, and ensuring data is available where and when it's needed.
Daily activities may include:
- Writing scripts to automate data ingestion
- Cleaning and transforming raw data
- Monitoring data pipelines and resolving issues
- Collaborating with analytics teams to understand their data needs
- Managing databases and ensuring secure access
Their work is foundational, making sure that everything downstream (like analysis or reporting) runs smoothly.
What a Typical Day Looks Like for a Data Scientist
Data scientists dive into available data and work toward delivering actionable insights through analysis and modelling.
Daily activities may include:
- Exploring data sets to identify trends and anomalies
- Preparing data for analysis through cleaning and feature engineering
- Running machine learning models to predict outcomes or classify data
- Visualising findings using charts and dashboards
- Presenting insights to decision-makers in meetings or reports
While engineers work more in the background, scientists are often “front-facing”, helping shape business or product strategy.
Collaboration in Action
Data scientists depend on engineers for high-quality, accessible data. In return, engineers build better systems when they understand what kind of data is most valuable.
Together, they form a feedback loop that improves both the infrastructure and the insights delivered. This collaboration is essential in any organisation that relies on data to innovate or scale.
