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Other data processing and communication engineers

How to become a Data Engineer

Also called: Big data engineer

Organises and manages huge amounts of data so it can be analysed, and builds and runs the systems that make large, complex data usable.

Main tasks

Workers in this job say university is common. From job tag's survey of people in each job, who could give several answers. It shows what workers see as common, not how many workers hold each level.

How to become one

Many new graduates hired are from science and engineering degrees or postgraduate study, but there are also many humanities graduates who worked with data in subjects such as psychology or economics. Some are IT graduates of a kosen (college of technology) or senmon gakko (vocational college). Some companies hire new graduates as systems engineers and then train some of them as data engineers. Many mid-career hires move from IT engineering jobs such as systems engineer. Detailed knowledge of a particular field, such as finance, healthcare, manufacturing or education, is an advantage when hiring mid-career. There are overseas certifications run by private companies (such as Google Professional Data Engineer), and holding one shows data engineering skills in Japan and abroad. They are not data engineering qualifications as such, but the Information-technology Promotion Agency (IPA) Database Specialist and Systems Architect exams show related knowledge. For new graduates, many companies provide training as needed in basic skills (programming, maths, data analysis, databases, distributed processing, machine learning, AI and so on), business knowledge (such as understanding clients' work to grasp the background of the data) and project management (team management and agile development). With faster development now expected, agile development skills are needed rather than the older waterfall approach. After this training you join projects in the company and build your skills on the job, and also by taking part in related communities such as Kaggle. As its home page says, Kaggle is 'Your Machine Learning and Data Science Community', bringing together hundreds of thousands of people around the world working in machine learning and data science. Later, some people follow a management path, managing projects and then groups, while others deepen their expertise and become experts. Some go on to become data scientists or AI engineers. Data engineers need maths skills such as calculus, linear algebra, probability and statistics; programming skills in Python, Scala, Java, R and similar; and the skills to design, build and run big data analysis environments on cloud platforms such as Amazon and Google. Knowledge of distributed processing of large data using tools such as Hadoop is also needed. With AI development so active today, knowledge of machine learning is often required too. Knowledge of the business area the data comes from is very important; without it you cannot tell what data to use or how to process it. You also need to keep up with technology trends and social and economic developments. Generative AI is increasingly used to write the programs needed for collecting and analysing data: you make a rough version with generative AI, then fix and add to it, test it and use it. You need patience and persistence to process huge, complex data carefully. In analysing data you must also check your own assumptions and look at your ideas objectively. Curiosity about technology and analysis and a willingness to try new methods are also needed.

Ways in

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Questions people ask

How do I become a Data Engineer?

Many new graduates hired are from science and engineering degrees or postgraduate study, but there are also many humanities graduates who worked with data in subjects such as psychology or economics. Some are IT graduates of a kosen (college of technology) or senmon gakko (vocational college). Some companies hire new graduates as systems engineers and then train some of them as data engineers. Many mid-career hires move from IT engineering jobs such as systems engineer. Detailed knowledge of a particular field, such as finance, healthcare, manufacturing or education, is an advantage when hiring mid-career. There are overseas certifications run by private companies (such as Google Professional Data Engineer), and holding one shows data engineering skills in Japan and abroad. They are not data engineering qualifications as such, but the Information-technology Promotion Agency (IPA) Database Specialist and Systems Architect exams show related knowledge. For new graduates, many companies provide training as needed in basic skills (programming, maths, data analysis, databases, distributed processing, machine learning, AI and so on), business knowledge (such as understanding clients' work to grasp the background of the data) and project management (team management and agile development). With faster development now expected, agile development skills are needed rather than the older waterfall approach. After this training you join projects in the company and build your skills on the job, and also by taking part in related communities such as Kaggle. As its home page says, Kaggle is 'Your Machine Learning and Data Science Community', bringing together hundreds of thousands of people around the world working in machine learning and data science. Later, some people follow a management path, managing projects and then groups, while others deepen their expertise and become experts. Some go on to become data scientists or AI engineers. Data engineers need maths skills such as calculus, linear algebra, probability and statistics; programming skills in Python, Scala, Java, R and similar; and the skills to design, build and run big data analysis environments on cloud platforms such as Amazon and Google. Knowledge of distributed processing of large data using tools such as Hadoop is also needed. With AI development so active today, knowledge of machine learning is often required too. Knowledge of the business area the data comes from is very important; without it you cannot tell what data to use or how to process it. You also need to keep up with technology trends and social and economic developments. Generative AI is increasingly used to write the programs needed for collecting and analysing data: you make a rough version with generative AI, then fix and add to it, test it and use it. You need patience and persistence to process huge, complex data carefully. In analysing data you must also check your own assumptions and look at your ideas objectively. Curiosity about technology and analysis and a willingness to try new methods are also needed.

How much does a Data Engineer earn in Japan?

Average annual income in the private sector, bonus included, is ¥6,097,600 (Ministry of Health, Labour and Welfare). The figure is for the wage survey group Other data processing and communication engineers, which covers several jobs.

Do you need a degree to become a Data Engineer?

Usually. In this job's census occupation group, 61% of workers are university graduates.

Will AI change the work of a Data Engineer?

Generative AI could change many tasks in this job, which usually reshapes the work more than it replaces it, by the ILO's global estimate.

Sources

独立行政法人労働政策研究・研修機構（JILPT）作成 職業情報データベース 解説系ダウンロードデータ（IPD_DL_description_7_01.xlsx）ver.7.01 職業情報提供サイト（job tag）より2026年10月4日にダウンロード（https://shigoto.mhlw.go.jp/User/download）を加工して作成

独立行政法人労働政策研究・研修機構（JILPT）作成 職業情報データベース 簡易版数値系ダウンロードデータ（IPD_DL_numeric_7_00.xlsx）ver.7.00 職業情報提供サイト（job tag）より2026年10月4日にダウンロード（https://shigoto.mhlw.go.jp/User/download）を加工して作成

Sources: JILPT Occupational Information Database download data via job tag (解説系 ver.7.01 and 簡易版数値系 ver.7.00, downloaded 4 October 2026), processed by Peernovo; MHLW, Basic Survey on Wage Structure 2025; Statistics Bureau of Japan, 2020 Population Census; MEXT, School Basic Survey 2025; MHLW, Employment Referrals for General Workers (Hello Work statistics). Figures processed by Peernovo. English job names, translations and the matching of jobs to statistical groups, schools and degree fields by Peernovo.

Updated 4 October 2026