Welcome Pavlin Mavrodiev, CTO at Propulsion, and the Big Data Engineering program.

Welcome Pavlin Mavrodiev, CTO at Propulsion, and the Big Data Engineering program.

Welcome Pavlin Mavrodiev, CTO at Propulsion, and the Big Data Engineering program.

Published on 04-10-2019 by Dr. Pavlin Mavrodiev

1. You have a PhD in Data-Driven Modelling from ETH Zurich and many years of experience in industry. For the next step of your career, why did you become CTO at Propulsion Academy?
 
That’s a simple question to answer. Propulsion offered me a great opportunity to use my experience and shape its technology strategy, as it grows. Due to the unique design of its bootcamps, Propulsion is involved in many industrial collaborations developing various prototypes in the data science and full-stack fields. Guiding the development of products out of those endeavors is a major growth path for the company that I am excited to support.

2. From Data Science, Full-Stack Development, to Big Data Engineering (with you), Propulsion has quite an extension and ambitious set of programs. What did you like the most about Propulsion that made you sign on?

I love the main mission of Propulsion - to educate motivated and hard-working people and help them get jobs in their desired fields. I, myself, have always enjoyed teaching - transferring one’s knowledge is a very rewarding experience. I am definitely looking forward to do some teaching in Propulsion.

I have  also known Propulsion for some time now, so in a sense I am getting into an environment that I know and like. Previously, I taught parts of the Machine Learning module in their Data Science program and was also a guest speaker. This gave me an inside view of how the company operates. I met Nitin and Laurent almost two years ago and we have stayed in close contact ever since. We understand each other easily and work well together.

3. We kicked off the blog by mentioning your experience. Now’s your chance to go into more detail. So what actually did you do before Propulsion Academy?

After my PhD, I started working in the field of Big Data Engineering with the first version of Spark. Afterwards, I held positions as head of technology in a crypto-currency start-up and later as a data scientist. Data engineering and data science have given me a very complete view of the whole end-to-end machine learning pipeline - from data ingestion to model training and serving. In the light of our current growth plans, it is precisely this perspective that is a great fit between me and Propulsion.

4. You have been on board for over a month now so give us an inside look: how’s it going so far being in education and development at the same time?

It is quite challenging, but also very rewarding. It is true that while our main focus is on education, we are also trying to envision a sustainable product strategy, develop corresponding proof of concepts and approach potential customers. Fortunately we have great program managers who make it easy for me to teach data science and still retain the majority of my time for product development.

5. You’re also involved in a new program offered by Propulsion, Big Data Engineering. What is Big Data Engineering and why is it important ?

That is true! Starting in 2020, we are offering our new bootcamp on Big Data Engineering, in addition to Data Science and Full-stack Development. This is a program I designed, drawing on my experience as a big data engineer, but also as a data scientist.There are many things unique about this program! 

To start, it will be the first and only Big Data Engineering bootcamp in all of Europe! We’re not going into this blindly just to brag about being first. What really got us going where the number of job ads for Big Data Engineers that we see rising on a daily basis. Second, the program duration of 6 weeks (instead of 12) is different than the other two bootcamps. That’s because the program is more focused requiring the participants to have several years of programming experience and understand key concepts of Computer Science.

Now let’s get technical! Big Data Engineering deals with the ingestion, processing and storage of big data. Ingesting big data is a challenge because it’s characterized not only by its volume, but also by its heterogeneity and velocity. This means that enterprises nowadays need a new generation of distributed and fault-tolerant systems to make use of huge datasets, from gigabytes to petabytes, that come from a variety of sources, such as batch systems, web forms, event streams, etc. The engineering skills and talent needed to architect and implement reliable big data pipelines are in great demand and that is the main reason why we created this new program.

Big Data Engineering is crucial: it’s literally the engine that makes modern enterprises function efficiently. We live in a world of data in which almost all industries have seen an immense increase in the data they generate. Today data-driven insights from data scientists inform board-room decisions and entire business models are built upon data products. All of this starts with big data engineering.

6. Now that we’re done with the past and the present, what do you wish for Propulsion and for yourself in the future ?

The more intelligent, ambitious and conscientious people Propulsion can help start their careers, the more successful it will be. I hope we keep growing with new offerings and into new markets. If I could make a wild wish, it would be for a Propulsion Alumni network that will one day connect all those students, whatever positions they may hold, who all share valuable memories of Propulsion being there for them at the start of their careers. As for me, I hope to be part of the journey that leads to that.

7. That was nice and inspirational, but let’s get personal now. What does Pavlin like to do when it’s not Big Data Engineering or Data Sciencing? Give us the best for last!

Professionally, I am very interested in technology in general, so I do my best to stay informed about the latest developments. Perusing websites like Ars Technica, Hacker News, OSnews, and Slashdot is a regular activity for me.

Outside of work I am a big sports fan. I play football, do interval training, but most consistently dance traditional Bulgarian folklore. It’s ironic that despite being Bulgarian, I first got interested in traditional folklore when I came to Switzerland 10 years ago.

Without undue bragging, I could say that my sense of rhythm turned out to be pretty good, so dancing for me now is quite an intensive hobby. It turns out that teaching dancing and Data Science is not that different when it comes to dealing with people, so I think I am going to try to convince Laurent and Nitin to  offer dancing bootcamps as well (:laughs boisterously:)!

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