Data Science vs. Data Engineering: Lessons from My Journey

Ekene Eze · Improve this Guide

Many aspiring professionals wonder about the difference between data science and data engineering. Although both fields involve working with data, they focus on different aspects of the data lifecycle.

When I started my tech career, I was torn between data science and data engineering. I spent weeks researching and experimenting with projects to find where my strengths fit best. Both fields are closely related but yet distinct.

If you love working with data, solving complex problems, and using data to guide decisions, you should consider data science, but if you enjoy building and maintaining data infrastructure and systems, I suggest you consider data engineering.

In this guide, I will share the key lessons I learned, the challenges I faced, and how you can decide which career is right for you.

The table below summarizes the differences between data science and data engineering.

Differences between data science and data engineering

	Data science	Data engineering
Focus	Analyzing and interpreting data using statistical models, machine learning, and analytics to obtain value and make predictions.	Designing, building, and maintaining data pipelines and infrastructure for data storage and accessibility.
Skills	Machine learning, statistics, data visualization, and predictive models.	Knowledge of data infrastructure, database management, and cloud tools.
Tools	Python, R, SQL, Tableau, PyTorch, Power BI, Tensorflow.	Python, Scala, R, Java, Apache Spark, Kafka, Snowflake, Databricks.
Educational background	Computer science, statistics, and mathematics.	Computer science, computer engineering, and software engineering.
Career paths	Data scientist, Data analyst, Machine learning engineer.	Data engineer, Big data engineer, Data architect.

What is data science and data engineering?

When I worked on my first data-driven project, I realized how much I enjoyed working with raw data. But I also saw how much effort went into setting up pipelines and structuring data before I could analyze it. That’s when I understood the fundamental difference: Data engineers build the highways, while data scientists drive on them to find insights.

Data science is a field that combines mathematics, statistics, analytics, artificial intelligence, and machine learning to analyze large amounts of data to detect hidden patterns, generate actionable insights, and predict trends. As a data scientist, you’ll analyze and interpret complex data to help organizations and businesses make informed decisions.

On the other hand, data engineering involves designing and building systems for aggregating, storing, and analyzing data. As a data engineer, you’ll build data warehouses to empower data-driven decisions. You will focus on developing pipelines to collect, clean, and transform data, ensuring it is accessible for analysis by the data science team.

Key responsibilities of a data scientist

Your responsibilities as a data scientist will vary depending on the industry, company size, and project focus. To provide more context, I will explain the responsibilities of a data scientist using a project I worked on during my career. The project involved customer churn analysis for a client in the hospitality industry.

1. Business understanding: As a data scientist, you’ll work closely with stakeholders to understand the context and gain business insights. In the customer churn analysis project, I worked closely with the client to understand the business context and gain insights. This provided an in-depth understanding of the hospitality sector and the different terms used. Without this, solving the business problem would have been almost impossible.

2. Data ingestion: Your work as a data scientist involves gathering raw data from different sources. It is your responsibility as a data scientist to collect all the relevant data to solve the current business problem.

When I worked on the customer churn analysis project, I had to pull data from multiple sources (customer transaction logs, website analytics, and CRM tools). But before I could even begin the analysis, the data engineers had to build ETL pipelines to collect and clean this data.

3. Data processing and data storage: This is one of your core responsibilities as a data scientist. It involves cleaning and transforming the ingested data into suitable formats for analysis and saving data storage systems.

In the customer churn analysis project, the data ingested were unusable and had to be cleaned so they could be used for analysis. I used tools like Python and NumPy to process the data and saved the processed data in a database.

4. Data analysis: As a data scientist, you’ll analyze processed data using statistical analysis and data visualization tools like Matplotlib, Seaborn, and Pandas. You’ll explore the data to understand relationships, hidden patterns, characteristics, and anomalies. You will use charts and graphs to make the data more understandable and easier to comprehend.

   In the customer churning analysis project, I analyzed the data by identifying the number of returning customers, the customers who came only once, the day of the week with the most customers, and so on. With this analysis, I could identify a trend in the data, which helped in predictive model building.

5. Model building: As a data scientist, you’ll build predictive models and machine learning algorithms to forecast future trends. The machine learning algorithms you build can be categorized into two types:

   • Supervised learning: Examples include linear regression, decision trees, and k-nearest neighbors.
   • Unsupervised learning: Examples include clustering and dimensionality reduction.

6. Reporting: As a data scientist, it is your responsibility to interpret and communicate the results of data analysis and predictions. It is not enough to analyze and explore the data; you have to communicate them clearly so that the key stakeholders easily understand them, influence decision-making, and achieve business goals.

Skills and tools needed to succeed as a data scientist

To succeed as a data scientist, you need to have a balance of technical and analytical skills. Some of these skills and tools I recommend include:

1. Programming languages: As a data scientist, you should know some programming. The two most common programming languages data scientists use are Python and R.

2. Mathematics and statistics: To succeed as a data scientist, you must know mathematics and statistics. They help you understand your data and know which statistical model to apply. Knowledge of mathematical concepts like calculus, probability theory, and linear algebra are fundamental to your success as a data scientist.

3. Data visualization: This involves using graphs, charts, and maps to present data in an understandable and accessible format. It is important to know how to visualize data. Some of the tools used to create visualizations include Matplotlib, Seaborn, ggplot2, and Pandas.

4. Database management systems: Database management systems are applications that interact with users, other applications, and the database to fetch and analyze data. As a data scientist, you will interact a lot with databases, and you should know how to write queries to communicate with them.

SQL is a programming language used to manage and communicate with relational databases. Examples of relational databases are MySQL, PostgreSQL, and Microsoft Server SQL. There are also NoSQL databases that store unstructured data. Examples include MongoDB, Neo4j, and Cassandra.

5. Machine learning and artificial intelligence: As a data scientist, you should have an understanding of machine learning. Machine learning can be divided into supervised, unsupervised, deep, and reinforced learning. Some key tools used by data scientists include Scikit-learn, TensorFlow, and PyTorch.

Key responsibilities of a data engineer

To explain the key responsibilities of a data engineer, I will use an IoT project I worked on to provide more context. The project involved developing an IoT data pipeline to transmit data from a customer’s IoT devices in the field to a storage system. Your key responsibilities as a data engineer include:

1. Data pipeline development: A data pipeline is a method of ingesting data from different data sources, transforming the data, and then transferring it to storage and processing systems. You are responsible for building and maintaining data pipelines as a data engineer. The different types of data pipelines include:

   • Batch processing pipelines
   • Data streaming pipelines
   • Data integration pipelines
   • Cloud-native data pipelines In the IoT project, the data engineers designed the data pipeline used to transmit data from the IoT devices to the data storage system. The pipeline was a messaging system into which the IoT devices published data, which was ingested and stored in a database.

2. Data architecture design: As a data engineer, you will build, design, and manage data lakes and warehouses for data storage and retrieval. You will work with cloud platforms (AWS, GCP, Azure) to develop scalable and reliable storage solutions.

3. Database management: As a data engineer, you’ll manage and optimize storage solutions, which include relational databases, document databases, and data lakes. These data storage solutions store big data for analysis and prediction for data integrity, performance, and accessibility.

4. ETL processes development: Extract, Transform, Load (ETL) is a process that cleans and organizes data from multiple data sources into a single and consistent data set for storage and further processing. As a data engineer, you are responsible for developing and maintaining the ETL process for proper data integration and onward passage of the data to be used by data scientists.

   The data from the IoT devices was in different formats, so the data engineers had to build an ETL process to transform the data for onward processing and storage.

5. Real-time data streaming: Data engineers process real-time data from different data sources, which can be triggered via events or observers. As a data engineer, you design systems that respond to real-time data, and use tools like Google Publish and Subscribe and Azure Publish and Subscribe to transmit and process it.

   The data sent from the IoT devices was real-time data, and the data engineers built the messaging system that streamed the data.

6. Data governance and security: Data engineers are responsible for compliance with data privacy laws. They implement data validation and integrity checks, which results in clean data and data reliability. They also make sure that sensitive user data is protected and not exposed without sufficient clearance.

Skills and tools needed to succeed as a data engineer

To succeed as a data engineer, you need a combination of programming, data management, data pipelines, and cloud computing skills. I will explain the skills below.

1. Programming languages: You should know programming and scripting. Python is one of the most common programming languages you will use as a data engineer. Python is used for data processing, creating ETL pipelines, and automation. Other programming languages used are Java, SQL, and Scala. Bash scripting is also a necessary skill you should have for workflow automation.

2. Databases: As a data engineer, you are expected to know how to design and maintain database management systems. You will use relational databases and NoSQL databases to store and retrieve data. Examples of relational databases include PostgreSQL, MySQL, and Microsoft SQL Server. Examples of NoSQL databases include MongoDB, Redis, and DynamoDB. NoSQL databases are used to store unstructured data.

3. Data warehouses: A data warehouse is a system that gathers data from different sources and stores them in a central location. It help prepare data for data analytics, machine learning, and artificial intelligence.

   As a data engineer, you should know how to work with data warehouses and also design and maintain them. There are cloud-based data warehouses and on-premise data warehouses. Cloud-based data warehouses are provided by cloud platforms and they include Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. Examples of on-premise data warehouses include SAP BW/4HANA and IBM Db2 Warehouse.

4. Data lakes: Data engineers use data lakes to store structured and unstructured data in their original formats. They store various types of data in different formats and provide a central repository for data analysis workloads. Examples of data lakes include AWS S3, Azure Data Lake, and Google Cloud Storage.

5. ETL processes: Data engineers use ETL processes to automate the storage and retrieval of data in a database. The data is extracted from its source, transformed into the required format using automated scripts and programs, and then loaded to its destination. As a data engineer, you should know how to design ETL processes and build data pipelines.

Data scientist vs. Data engineer: What career opportunities are available to you?

Both professionals are in high demand in several industries, including health care, tech, finance, and retail.

The typical career path in data science includes:

• Junior data scientist
• Data scientist
• Senior data scientist
• Machine learning engineer

The career path of a data engineer is similar to that of a data scientist. A typical data engineering career path includes:

• Junior data engineer
• Data engineer
• Senior data engineer
• Data architect
• Senior data architect

Salaries and job market trends

One thing that surprised me was how salaries vary depending on industry and specialization. In my experience, companies in finance and healthcare tend to pay data scientists more, while big tech firms offer strong compensation for data engineers working on large-scale infrastructure.

According to Indeed, the average annual data scientist salary in the US is $125,156 with a range between $79,612 and $196,755.

The average data engineer salary in the US is $125,758, with a range between $82,340 and $192,069.

Both data scientist and data engineer roles are in high demand with the rise in AI. Companies are constantly hiring data scientists and data engineers. On Indeed, there are more than 10,000 openings for data scientists and more than 5,000 in the US alone.

Data scientists and data engineers will also be very instrumental and in high demand in the future. According to the US Bureau of Labour Statistics, there will be 20,800 new openings for data scientists each year for the next decade.

Data science vs. Data engineering: Which path fits you better?

Deciding whether to pursue data science or data engineering depends on your interests, strengths, and career goals.

If you enjoy solving analytical problems, working with algorithms, and guiding business decision processes, then you should consider data science. Do you enjoy building large-scale systems and data infrastructure and ensuring data pipelines run smoothly? Then, you should consider data engineering.

Do you understand statistics, mathematics, data analysis, and visualization well? Data science is well-suited for you. Data engineering is the right fit if you have strong programming skills and are knowledgeable in system design and architecture.

Next steps

If I had to start over, I’d begin with small projects. My first real learning moment came when I built a basic recommendation system for movie ratings using Python and Pandas. If you’re considering data engineering, setting up an ETL pipeline with Apache Airflow is a great starting point. Don’t just read—build.

You should also follow a structured learning path. roadmap.sh provides you a structured data science roadmap where you can track your progress and share it on your profile. You could also customize your roadmap based on your learning needs.