Data Engineers vs Data Scientists: A Comparative Exploration

Albert Christopher
6 min readSep 8, 2023


Data engineers and data scientists are two different job roles that require different skill sets, but they collaborate closely to harness the power of data for meaningful insights and business decisions.

Although they have similar positions in the fields of data analytics and data science, data engineers and data scientists have different duties and skill sets. Contrary to popular belief, these two positions have distinct functions inside a data-driven business. Consider that a data team has been tasked with developing a data model. Any model might be employed, but let’s pretend it’s one that predicts client retention. The data engineer creates and maintains data collection methods for the model’s data, whereas the data scientist creates the model’s structure and algorithms. If you’re someone looking for a data science career, it’s critical to know how these two areas differ from one another and which one would be more suitable for someone with your qualifications and interests.

👉Role of Data Engineers Vs. Data Scientists

Data Engineers

  • Data engineering professionals facilitate the practical implementation of data science solutions.
  • They integrate data science models seamlessly into the data processing pipeline.
  • Data engineers must balance data accessibility, system flexibility, and system performance optimization.
  • Their expertise involves selecting suitable languages, databases, and technologies for solution implementation.
  • Scripting is extensively used by data engineers to connect data processing components.
  • They play a crucial role in ensuring the efficient integration and operation of data-driven solutions within the organization.

Data Scientists

  • Data scientists primarily engage in direct analytical tasks, focusing on data processing, modeling, and problem-solving.
  • They generate solutions for specific challenges, leveraging their analytical skills and expertise in statistical concepts.
  • Data scientists explore the boundaries of the data science field to find innovative approaches for addressing complex problems.
  • A strong foundation in mathematics and statistics is crucial for data scientists to succeed in a data science career, and also to attract reputable job opportunities.
  • They are responsible for creating data models to extract valuable insights from data sets.
  • Data scientists apply rigorous mathematical and statistical methodologies to analyze data comprehensively.

👉 Skills of Data Scientists vs. Data Engineers

Data Scientists

  • Statistical analysis
  • Machine learning
  • Data visualization
  • Domain knowledge
  • Data cleaning
  • Hypothesis testing

Data Engineering

  • Database management
  • Data Pipeline
  • SQL
  • Data modeling
  • Data warehousing
  • Cloud services

👉 Salaries of Data Engineers vs. Data Scientists

The Bureau of Labor Statistics (BLS) reported that data scientists had a median annual salary of roughly US$100,900, while Glassdoor indicated that data engineering professionals earned an average annual salary of around US$142,000. Salary numbers may fluctuate based on variables like geographic location, professional background, and industry. Nevertheless, these data points offer a broad perspective on the typical salary range for both data scientists and data engineers.

👉 Tools Used by Data Engineers vs. Data Scientists

Let’s delve into this section by examining individual case studies.

Scenario: A business that operates online wants to create a real-time clickstream analytics system to examine website user behavior. For the benefit of data scientists and analysts, they must gather, evaluate, and store clickstream data from diverse sources.

Tools Used by Data Engineers

Data engineers utilize essential tools for building robust data pipelines. Apache Kafka is a cornerstone, enabling real-time data streaming and event collection from diverse sources. Apache Spark handles data processing tasks, such as filtering, aggregating, and transforming incoming clickstream data. Hadoop HDFS, a distributed file system, efficiently stores vast datasets. SQL and NoSQL databases like PostgreSQL, MongoDB, or Cassandra manage structured and unstructured data. Orchestrating and scheduling data pipeline workflows is streamlined with Apache Airflow, while data processing logic is coded in Python, Scala, or Java. Docker and Kubernetes facilitate the deployment and management of data pipeline components. Apache NiFi ensures smooth data routing, transformation, and flow management within the pipeline. Finally, monitoring tools like Prometheus and Grafana maintain the health and performance of these intricate data pipelines, ensuring their reliability and efficiency.

Scenario: By identifying consumers who are likely to discontinue their subscriptions, a telecommunications corporation hopes to lower customer churn. They have gathered historical customer information about past clients, such as usage trends, client demographics, and previous turnover statistics.

Tools Used by Data Scientists

Data scientists rely on a range of tools for their work. Python serves as their primary programming language, forming the basis for data analysis and machine learning. Jupyter Notebook enables interactive data exploration and model development. Pandas aid in data manipulation and preprocessing. Scikit-Learn is essential for building machine learning models, including classification algorithms like logistic regression and random forests. Matplotlib and Seaborn are employed for data visualization. Deep learning frameworks like TensorFlow or PyTorch are used for advanced models such as neural networks. Scikit-Learn and XGBoost assist in model evaluation and hyperparameter tuning. For presenting findings to non-technical stakeholders, data scientists may turn to Tableau or Power BI, to create interactive dashboards.

👉 Certifications for Data Engineers

👉 Certifications for Data Scientists

👉 Collaboration of Data Engineers vs. Data Scientists

Data analytics and machine learning require close cooperation between data engineering professionals and data scientists. Assuring that the data infrastructure is reliable, scalable, and tailored for the unique requirements of data scientists, data engineers play a fundamental role in this cooperation. They take care of the data’s ingestion, cleaning, transformation, and storage, making sure it is reliable and accessible. Data engineers commonly work with technologies like Apache Hadoop, Spark, and other databases to create data pipelines that simplify the transit of data from source to destination.

On the other side, data scientists focus on learning from and adding value to data. They use statistical and machine-learning techniques to evaluate data, produce prediction models, and offer practical insights. Since their work relies on structured, prepared data, data scientists and data engineers must collaborate closely. Data engineers help data scientists by providing a clean and organized data repository, allowing them to focus on the analytical aspects of their work. Effective communication and collaboration between these two jobs are essential for the success of data-driven initiatives because they ensure that data scientists have access to the appropriate data at the appropriate time, resulting in more accurate and pertinent results.

👉 Choosing the Right Career Path

If you’re a graduate or an IT professional looking for a rewarding data science career, you must first understand the principles of data science. A solid mathematical foundation and an examination of theoretical concepts are essential factors for success in this discipline. Statistics, linear algebra, and other mathematical disciplines are required. Delving into published articles will also provide a comprehensive view of the field’s interconnection.

On the other hand, if you appreciate writing automation scripts, optimizing pipeline components for efficiency, and overcoming technical issues, a career as a data engineer may be a good fit for you. Data engineering is a highly specialized area, and while a basic understanding of mathematics is helpful, it is not required.


Selecting your career path in data science requires careful consideration if you are a professional aiming for success. Begin by assessing your current skill set and identifying the skills you are eager to acquire. Aspiring data scientists should focus on mathematics, statistics, and theoretical concepts to derive insights and tackle complex challenges. Conversely, data engineers thrive in crafting automation scripts, optimizing pipelines, and ensuring data reliability. Before you decide, assess your skills and envision your goals. Both roles are integral to data-driven decision-making, offering vast opportunities. Afterward, make an informed choice that aligns with your goals, whether it leads you toward becoming a data scientist, data engineer, or any other role within the realm of data science.



Albert Christopher

AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning