First of all, please tell us something about yourself: What hashtags best describe you?
#DataEngineering #DigitalTransformation #Innovation #BI #Automation
Please tell us more.
My daily work revolves around data, digital processes and automation. As a data engineer, I structure data in a way that helps businesses make better decisions. That’s why I find digital transformation to be so exciting because it allows me to replace current processes with new technologies and approaches. Business intelligence (BI) is essential for data-driven decisions, and automation helps to minimise repetitive tasks and increase efficiency.
Let's talk about your professional activities: What do you do at Schindler Elevators Ltd?
I’m responsible for developing and implementing digital solutions at Schindler Elevators Ltd in Switzerland, where I lead projects in data engineering, BI, AI and robotic process automation (RPA). My focus is on optimising data flows, automating processes and providing data analytics to help the company make better decisions.
What did you do before and why did you join Schindler Elevators Ltd?
I originally worked in the hospitality industry and graduated from the Hotel Management School in Lucerne. During the COVID-19 pandemic in 2020, I decided to change careers and thus discovered my passion for data and digital processes. The Data Science and Data Engineering programme gave me a lot of experience with IT projects, and I eventually got the opportunity at Schindler to develop innovative, data-driven solutions.
What is the most exciting part of your job?
The variety! Each day is different – I collaborate with different business units, develop new digital solutions, and analyse complex data structures. It's especially exciting when raw data is turned into real value for the company. I also love using innovative technologies like Azure Data Factory, Databricks, Power BI, or RPA tools to optimise processes.
Which data science skills are particularly in demand in your job?
In my role, SQL and Python skills are essential, as I work with databases and develop ETL processes on a daily basis. Additionally, cloud computing skills (especially Azure) and experience in business intelligence (Power BI, Qlik) are important. But it’s also crucial to be able to think analytically, understand business processes and know how to design complex data models.
Do you see yourself more as a techie, an analysis whiz, a creative genius, a management superhero or a brilliant all-rounder?
I’d describe myself as a mix between a techie and a data nerd. I love experimenting with new technologies and developing solutions, but I also really enjoy deep-diving into data analysis to uncover new insights.
What fascinated you most about the MSc in Applied Information and Data Science programme?
The combination of theory and practice – especially the projects where we analysed real datasets and developed solutions. During my studies, my horizons broadened a lot in a short time. The modules offered were up to date and very practical, which made it easier to apply what I learned directly in my professional life.
What are the biggest challenges in your job right now?
One major challenge is keeping up with the rapid pace of technological change. There are constantly new tools, frameworks and methods that could potentially be valuable to our work. Also, aligning business requirements with technical possibilities often requires a lot of communication and persuasion.
What advice would you give to someone who wants to do the same thing as you?
Being open to trying things out! Data engineering and digital transformation are incredibly dynamic fields, so it pays to keep learning and experimenting with new technologies. It’s also important to develop a solid understanding of business processes in order to apply data-driven solutions in a meaningful way.
Finally, what new hashtag are you aiming for in future?
#AIIntegration – artificial intelligence will play an even bigger role in the coming years, and I want to develop my skills in this area further to create innovative solutions.
We would like to thank Alexis Lüthi for his dedication and for sharing these valuable insights.
