It’s time for another one of our interviews where we put our data detectives in the spotlight. So, we caught up with Renato, our data team lead. He allowed us to venture into his data science and engineering world. Not only is he the data team lead, but he also plays an important role in business development processes. From his daily activities to work challenges, he covered all the topics for us to get to know him better.
Tell us who you are and what you do in Digital Poirots and Deegloo.
I perform a couple of roles here in Digital Poirots and Deegloo. My engineering half is focused on data science and engineering, where I perform the role of a team lead. As a co-founder, I have an entirely different role which is related to business development processes.
What does your typical day look like?
It’s a mix of business and engineering tasks. 🙂 Repetitive tasks are rare as each day brings some new challenges to overcome. Some typical data-related tasks include data collection and processing, development of data pipelines, analysis and visualizations, and development of prediction models.
What made you decide to develop your career in this field?
My interest in math started during the second half of elementary school and increased in high school where I had a great teacher.
That pushed me toward the Faculty of electrical engineering and computing where I found myself in the field of computer science.
What drives you in your work?
The sense of purpose. It’s a great feeling when you know that the work you did made an impact.
What’s the best part about your position?
The fact that every day brings some new challenges.
What don’t you like about your job?
The fact that every day brings some new challenges. 😅
What’s the biggest mistake you’ve made in your career or what ups moment you had?
This happened quite a while ago, at the beginning of my career. I was doing maintenance of the ETL process that was implemented with the Pentaho Data Integration tool. There was a task to update the list of emails that receive certain notifications from the ETL pipeline. Pentaho defines the pipeline as a sequence of transformations and jobs, where each one of them is defined as a separate XML file.
I checked the files and found out that emails were hardcoded. This meant I could not solve the task just by changing environment variables. As we had a lot of files that required the change, I decided to write a Python script that parses XML and replaces hardcoded emails with a new parameter. Because of a bug in the script, I ended up replacing not only the emails I should have but others as well. Hopefully, we had a backup that I created before script execution, which enabled fast rollback.
In the end, it took me more time to write the script, find the issue, and fix it than it would take me to do all of the changes manually in the first place. Since that day I am way more careful and reserved about applying automated processes to such sensitive tasks. Automation is a good thing, but there are some scenarios where manual intervention is a better option.
What drives you crazy about your job or in your daily activities?
Too much multitasking. In these situations, it’s best to prioritize tasks and reorganize the work. Otherwise, you usually end up with stress and unfinished business.
Which technology or tech stack do you like the most?
For data engineering, I mostly combine SQL with ETL development tools. Depending on the project and requirements, ETL pipelines are done through no-code tools like Pentaho Data Integration or code-based technologies like Airflow.
When talking about data science and machine learning, it’s mostly related to Python-based stack and tools like Pandas and scikit-learn.
For the visualization part, I mostly use Tableau which enables me to create good-looking, interactive, and dynamic visualizations in no time.
All solutions we create are hosted on the AWS cloud, where services like RDS, EC2, S3, and Sagemaker come into play.
What advice would you give to someone entering this field?
I would recommend learning in a structured way. This is the advice I got from one of my favorite university professors Jan Šnajder, who lectured to me on Machine learning during my studies.
He compared the learning process with books. Each day we learn, we read a new book. Once we are done with the lecture, we put the book on the shelf. As time passes, more and more books will be on the shelves, and we would forget the details that are written in them. But when the time comes, and we need some information, we know which book it belongs to. Additionally, we know the relationships between the books, which allows us to group concepts and establish a hierarchy between them.
This probably works for other fields as well, but I found it especially important in the fields of data science and machine learning.
Do you have any funny or interesting stories that happened here in the company?
The funniest thing that happened to me recently is that I almost tipped over from pedal Go Kart during the race we had on our team building. After a few meters of 2-wheel driving, I ran out of the track and crashed into the barriers.
I guess it was funnier for the audience than for me.
Your favorite person to work with?
This is the hardest question here. I can’t decide on one person, because everybody I work with at the company is professional and pleasant to work with.
If you can compare your job to one movie or show, what would it be?
I like Interstellar very much. Doing our data detective work sometimes reminds me of Murph.