Data Scientist vs. Data Engineer: Role Differences for the IT Passport Exam

Data-Related Job Roles

In recent years, the volume and complexity of data have grown rapidly. As a result, professionals who specialize in data have become indispensable. The three main roles are data scientist, data engineer, and data analyst. While these roles overlap in some areas, their responsibilities are clearly distinct.

Role Comparison of the Three Job Types

Data Scientist

Data scientists use statistical and machine learning techniques to solve business problems. Specific tasks include churn prediction, demand forecasting, and building recommendation systems. Required skills include programming languages like Python and R, machine learning, statistics, and domain knowledge. A balanced combination of "business × statistics × engineering" is essential.

Data Engineer

Data engineers design, build, and maintain the infrastructure for collecting, processing, and storing data. For example, building data lakes, data warehouses, and ETL pipelines are typical tasks. Essential skills include SQL, Spark, Hadoop, AWS/GCP, and Airflow. Their role is to prepare data so that data scientists can focus on analysis in a "ready-to-use" state.

Data Analyst

Data analysts use data to improve operations, evaluate initiatives, and perform KPI analysis. Examples include measuring marketing effectiveness and analyzing A/B tests. Required skills include SQL, BI tools, visualization, and basic statistics. Acting as a bridge between business and technical teams, the ability to clearly communicate analysis results is also important.

Common Skill: SQL

SQL is a fundamental skill required across all three roles. It is the language for communicating with databases and is used in every data-related workplace. You can learn more in Relational Databases and SQL Basics.

Positioning in the DX Context

Data scientists and data engineers are core professionals in big data and AI utilization (see What is DX?). The "DX Promotion Index" established by the Ministry of Economy, Trade and Industry (METI) also identifies securing human resources as an essential element. However, Japan continues to face a serious talent shortage, with METI estimating a shortfall of up to 790,000 people by 2030.

Career Paths

One example career path is moving from data analyst to data scientist, which requires learning machine learning. Another path is transitioning from data analyst to data engineer, moving toward infrastructure building. However, note that roles are not always clearly divided, and in some organizations, people often take on multiple responsibilities.

Key Points for the IT Passport Exam

The exam tests your understanding of the differences between the three roles, the main tasks of a data scientist, and the correspondence between required skills and job types. In particular, be able to accurately distinguish the scope of work for each role.

Typical Past Exam Question Patterns

• "Which role extracts value from data?" → Data Scientist
• "Which role builds data infrastructure?" → Data Engineer

Related Terms

• AI and Machine Learning (AI and Machine Learning Basics)
• Big Data (NoSQL and Big Data)
• Data Warehouse (Data Warehouses and BI)
• MLOps (What is MLOps?)

Study Tips

A helpful way to remember these three roles is with the keywords: "Analysis (Analyst), Model (Scientist), Infrastructure (Engineer)." Also, since SQL is essential for all roles, make sure you have a solid grasp of SQL basics. Understanding the combination of DX promotion and talent shortages will also serve you well on the exam.

Summary

For data-related questions on the exam, correctly understanding the roles and required skills of these three job types is key to scoring points. If you want to practice the Technology domain comprehensively, check out the Technology Category Summary. To test yourself in a simulated exam format, try the Practice Exam.