systems to allow the better processing of information. Screened data will then be forwarded to the Data Analytics Team for further development into SCB products. The Analytics Engineering Team is therefore a critical function supporting all Data Science and Data Analyst teams within the organization.

We recently talked with a member of the Analytics Engineering Team, Damrong “Golf” Tongsiri, who shared with us what it took for him to be able to join the SCB Data Analytics Team, and talked about his current career as an Analytics Engineer.

Let's find out more about this “SCB Data Analytics New Joiner Journey” story to learn how a new team player can help fulfil the team and drive SCB’s transformation into one of Thailand’s leading tech companies.

What did you go through to become an Analytics Engineer at SCB?

Golf: It was not an easy task. I had to take a series of tests, including undergoing a tough interview. SCB's Data Analytics team is known to be made up of the crème de la crème of people with outstanding potential and expertise in the data field. I prepared for the interview round by reading books on statistics, data structure, and math to ensure that I would be able to answer questions confidently and correctly. I also practiced solving programing problems, such as algorithms and HackerRank.

The first round consisted of fundamental tasks to measure my grasp of software engineering skills, such as mathematical algorithms and coding. In the next step the tasks were harder and deeper. When completing an exercise, I felt it was quite challenging because the assigned programming questions were quite complicated. I had to solve math problems to find the most effective solution most suitable for the given scenario. It wasn’t just basic solutions. My “can do” attitude and strong confidence helped me pass all the tough tests.

Why did you choose to work with SCB?

Golf: I saw that SCB could give me the opportunity to learn how to design new solutions answering the needs of business units. Because SCB’s systems are involved in big data, I wanted to work and help design solutions, allowing teams to better analyze information and help turn information into practical products.

What was your inspiration for becoming an Analytics Engineer?

Golf: An Analytics Engineer is responsible for turning data into bank products, and working in the data field has always been my aspiration. Working with data is interesting because information is something that is derived from human behavior. If we can collect a lot of information, we can better understand humans and create better products meeting their needs.

What is it like working as an Analytics Engineer?

Golf: As a machine learning engineer, I primarily deal with both software engineers in tandem with machine learning models and data pipelines to feed useful data to the Data Science team. We also work with business units to come up with ideas and solutions for product development.

The Analytics Engineering Team is also responsible for ensuring the Bank's data management system is as fast and efficient as possible. This reduces the cost of running different models in the cloud.

Tell us more about your current projects.

Golf: The Analytics Engineering Team has contributed greatly to a number of projects, making a huge impact on the Data Analytics Team. I am currently developing Feature Store, a new platform for processing and storing data. We are responsible for creating a system that controls the processing of new information. This will help the Data Science Team stay up-to-date and be able to quickly use data to create new models.

I am also working on other projects, such as the Robinhood app, for which I oversee the Search and Recommendation systems. The Analytics Engineering Team's job is to implement models developed by the Data Analytics Team on the real system, which must be closely monitored to ensure that the models, including the data pipeline, are functioning normally. I have to improve codes to ensure maximum efficiency.

How does it feel to be part of the Data Analytics team?

Golf: Many people have the impression that large organizations are very strict. In fact, our team is very friendly and flexible, which I felt from the moment I came in for the interview. Working and sharing moments with teammates was pretty much in line with what I initially expected.

Overall, I give the experience a full ten points, because my work corresponds with the scope that was discussed from the beginning. Everything is fine, and it's a very friendly working environment.

What challenges do you face at work?

Golf: Working in SCB's Data Analytics Team is not just about data. There are also many fields of science blended together, such as business administration, finance, and computing. My work requires a variety of financial formulas and time series data, such as how the Bank’s inventory accounts are calculated.

We have to adapt old formulas to machine learning to ensure that it can meet the needs of the organization as much as possible. I wasn’t daunted by this task, and it only took me a short time to adjust to learning new things. In the beginning, senior team members would help by providing training sessions, advice, and real-life examples. I was also allowed to practice with actual work.

Apart from learning new things and upskilling through assigned projects, my communication skills have also been developed. It is a necessary soft skill for this new era where meetings are conducted online. The team has daily meetings for updating, consultation, and brainstorming ideas to come up with alternatives to meet the needs of both short-term and long-term projects.

Our team still has a lot of unanswered business and data science questions waiting to be answered, and there are many data-driver products to explore and create.