During my studies in Information and knowledge management, I’ve had summer jobs and a part-time job as an intern in software development and consulting. However, my career and my final thesis were not supporting each other so I decided to make some changes. I learned about Solita’s Data Academy from friends and online. I decided to apply after hearing how it provides capabilities to work as a data engineer and connects people in the community. Luckily I got in so I had time to finish my thesis and I could start my career as a data engineer.
When applying to Data Academy, I had to choose between data engineer, analytics, integrations and master data management tracks. All of them are interesting to me but I chose the data engineer track as I wanted to improve my technical skills and learn more about data pipelines and warehousing. After all, my father always told me to start my career from the warehouse floor, so I chose the data warehouse.
Data Academy started with three weeks of common lectures and practices where we got to know other academians and the ways of working at Solita. Some of the lectures, such as Lean, data modeling and Python were familiar from my studies, but most of them were new as they focused on the tools and the ways of working specifically at Solita. The next three weeks were specialising in the chosen tracks, where Data Engineers and Analysts started working with SQL, Python and Agile Data Engine, which is a management platform for data warehouses in the cloud, and it’s the perfect tool for automated DataOps. ADE was invented by Solita and it was completely new for me so it was nice to learn how to use it. SQL and Python were familiar to me already, but I hadn’t used them together in my previous jobs, so it was interesting to solve practice assignments with them. SQL is such an important language in data engineering, so if I went back to school, I would study databases and SQL a lot more.
After the Academy, I was assigned to a project where I am working on data warehousing. Some of the most common tasks are to create data pipelines from source systems to reporting, add entities and attributes to data models and schemas and solve issues that arise from time to time. SQL is an important part of my job, but in my opinion, the most important skill is to learn new things quickly and search for information either online or by asking a colleague. After a few occasions of banging my head against a wall and finally asking for help, I’ve learned to solve issues more efficiently.
Three points for becoming a Data Engineer:
- Learn the most common technologies – Python, SQL and relational databases are a good start, where it is easy to expand your knowledge to other technologies later.
- Be active – When you encounter some unknown issue or error, search for information online from official documentation and forums or ask a colleague. Your friends are more than happy to help.
- Get comfortable with uncertainty – There will be a lot of things that you don’t know even exist, so keep calm and carry on!
Data Academy starts twice a year in spring and autumn, so stay tuned for the next application period later this year. You can read more about the Data Academy here. Make sure to check out other interesting topics from our previous blog posts!