In this article, one of the foreign-national members of Avaxia talks about how he came to join the company, what he has felt working in Japan as a junior engineer, what he is currently studying, and his future goals.
■ From Data Science to AI
I majored in data science at university. Before joining Avaxia, I worked at a factory of a major automaker and developed web applications in C# at an inspection company (lab).
I chose to study data science because I have always loved computers, and I strongly sensed the future potential and growing demand in the data field, especially with the advancement of AI.
At first, I imagined I would do work associated with traditional data science, such as data analysis, visualization, and creating graphs. After joining Avaxia and getting the opportunity to work as part of an AI-focused team, my interests broadened further. Specifically, I worked on backend development for AI applications using Python, including API development, building processing flows with LangChain and LangGraph, and information retrieval using RAG (Retrieval-Augmented Generation). I was also involved in building systems that retrieve necessary information from external systems, organize it into a form AI can use, and deliver it as search results or reports.
Through these experiences, I learned that to make AI useful in real business operations, the backend mechanisms that connect data and systems matter as much as the model itself. There was a great deal to learn, and I came to find the pace of change and the possibilities of the field appealing.
Today, AI is one of the fields I want to study more deeply going forward.
■ Current Studies and How They Connect to My Past Experience
Recently, I had a valuable opportunity to learn about data engineering.
Specifically, I studied technologies such as Kafka, ClickHouse, and NiFi, and deepened my understanding of ETL and data pipelines: how data is ingested, processed and transformed, then stored or delivered where it is needed. For example, I learned an end-to-end flow in which NiFi collects and organizes data, Kafka passes it along, and ClickHouse stores it for analysis.
In the data science I had studied previously, I was mainly interested in analyzing and visualizing data and using statistics and machine learning to find trends and characteristics. Data engineering, on the other hand, asks where the data needed for that analysis should be collected from, how it should be processed, and how to make it stable and ready to use. In other words, while data science asks “what can we learn from the data?”, data engineering supports the field by asking “how do we prepare and deliver usable data?” Learning this different perspective was a fascinating experience.
Currently, I am focusing on learning Boomi, a data integration platform with which Avaxia has a partnership. With Boomi, I am systematically studying topics such as the basic architecture of AtomSphere, data integration between systems, data transformation, and API integration.
My data science major and C# development in my previous job may seem, at first glance, to be in a different field from the AI and data integration I am learning now. Here, data integration means connecting data that resides in separate systems, converting its format as needed, and making it usable. In relation to AI, one example would be a mechanism that retrieves internal documents or information from external services via APIs, organizes it into a form AI can search and reference, and returns the results to an application or report. I feel that the AI backend development I have done so far and the Boomi data integration I am learning now share common ground.
In addition, I think that the logical thinking I gained through programming, and the fundamental approach to handling data, have been very useful when learning new technologies.
There is still much to learn about Boomi and other new tools and AI technologies, but I am working every day to build on my experience, understand the overall flow of the system step by step, and respond flexibly to new challenges.
■ The “Hou-Ren-Sou” and the Importance of Communication
Beyond the technical side, what I found challenging while working in Japan was the business culture of “hou-ren-sou” (reporting, updating, and consulting) and the way daily reports are written.
Especially at first, I was often unsure when, to what extent, and how I should share my progress with my supervisor and team.
When facing a technical problem, I can research it and find a solution on my own. Communication, however, requires considering the other person’s position and circumstances, which makes it a different kind of difficulty.
For this reason, through daily reports and communication within the team, I now try to explain as clearly as I can what I did today, where things stand, and what I am having trouble with or need to confirm.
I have also been putting more effort into studying business Japanese since this year.
By continuing to learn through trial and error in my daily work, I hope to gradually improve not only my technical skills but also the communication skills needed to be trusted as a member of the team.
■ My Goals at Avaxia
I am currently a junior engineer learning Boomi and data integration from the basics. In the future, I want to combine the data science and data engineering knowledge I have gained, my interest in AI, and the data integration skills I am now learning, so that I can become an engineer capable of solving a broader range of problems.
My goal is not just to carry out assigned tasks or write code, but to become an engineer who understands why each process is needed, where data comes from and where it goes, and what role a system plays within the whole business.
I also want to grow not only in technical skills but also in communication skills, so that I can become an engineer who my team and clients can trust and rely on.
There is still much to learn, but I will value each experience and continue to grow at Avaxia.
