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Google Cloud Data Heroes Series: Meet Tomi, a data engineer based in Germany and creator of the ‘Not So BigQuery Newsletter’

July 19, 2022
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Grace Yeung

Associate Product Marketing Manager

Mia Lerner

Associate Product Marketing Manager Intern

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Google Cloud Data Heroes is a series where we share stories of the everyday heroes who use our data tools to do incredible things. Like any good superhero tale, we explore our Google Cloud Data Heroes’ origin stories, how they moved from data chaos to a data-driven environment, what projects and challenges they are overcoming now, and how they give back to the community. 

In this month’s edition, we’re pleased to introduce Tomi! Tomi grew up in Croatia, and is now residing in Berlin, Germany, where he currently works as a freelance Google Cloud data engineer. In this role, he regularly uses BigQuery. Tomi’s familiarity with BigQuery and his passion for Google Cloud led him to creating the weekly newsletter Not So BigQuery, where he discusses the latest data-related information from the GCP world.  Additionally, he also works for one of the largest automotive manufacturers in Germany as an analyst. When not in front of the keyboard, Tomi enjoys walking with his dog and his girlfriend, going to bakeries, or spending a night watching television.

When were you introduced to the cloud, tech, or data field? What made you pursue this in your career? 

I always struggled with the question ‘what do you want to do in your life?. I attended school at Zagreb University of Applied Science for my information technology studies degree, but I was still unsure if I should become a developer, data engineer or something completely different.

A couple of years into working as a junior IT Consultant, I stumbled upon a job advertisement looking for a Data Analyst/Scientist. Back then, finding out that you can get paid to just work with data all day sounded mind-blowing to me. A dream job.

I immediately applied for the role and started learning about the skills needed. This is also where I gained my first experience with the Cloud as I signed up for a Google Cloud Platform free trial in February 2018. On the platform, there was a blog post describing how to run Jupyter notebooks in the Cloud. It interested me, and I went ahead and created my very first Compute Engine instance in Google Cloud Platform.

I didn’t get the job I initially applied for, but this was the trigger for me that set things in motion and got me to where I am now.

What courses, studies, degrees, or certifications were instrumental to your progression and success in the field? In your opinion, what data skills or competencies should data practitioners be focusing on acquiring to be successful in 2022 and why? 

Looking back at my university days, I really enjoyed the course about databases, which was partially because I had a really great teacher, but also because this was the first time I got to do something which catered to my then still-unknown data-nerdy side.

In 2019, I got my Google Cloud Certified Associate Cloud Engineer Certification which was a challenging and rewarding entry-level certification for Google Cloud. I would recommend considering getting one of these as a way of focusing one's learning.

One major change I’ve observed since working in the data field is the ongoing transition from on-prem to cloud and serverless. I remember a story from my early consulting days working in an IT operations team, when there was a major incident caused by an on-prem server outage. At some point one frustrated colleague said something like, ‘why do we even have to have servers? Why can’t it just *run* somehow?’ What sounded like a bit of a silly question back then turned out to be quite ‘visionary’ with all the serverless and cloud-based tech we have today.

What drew you to Google Cloud? Tell us about that process, what you’re most proud of in this area, and why you give back to the community? 

There is this great newsletter on Google Cloud Platform called GCP Weekly, run by a data community member named Zdenko Hrček that I really like. However, since the GCP ecosystem is growing at a rapid pace there are sometimes just too many news and blogs in a single week. I really struggled to catch up with all the new product updates and tutorials. That’s when I had the idea: ‘what if there would be a shorter newsletter with only news about BigQuery and other data-related tools’? Fast forward to today, my Not So BigQuery newsletter has more than 220 subscribers.

I was also inspired by the awesome content created by Priyanka Vergadia, Staff Developer Advocate at Google Cloud, such as her Sketchnotes series. I created the GCP Data Wiki, which is a public Notion page with cards for every database/storage service in GCP with useful details such as links to official docs, Sketchnotes and more.

What are 1-2 of your favorite projects you’ve done with Google Cloud’s data products? 

One of my first projects built with Google Cloud products was an automated data pipeline to get track data from the official Spotify API. I was looking for a data project to add to my portfolio and found out that Spotify lets you query their huge library via a REST API. This later evolved into a fully-serverless pipeline running on Google Cloud Functions and BigQuery. I also wrote a blog post about the whole thing, which got 310 claps on Medium.

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Additionally, the Not So BigQuery newsletter I created is actually powered by a tool I built using Google Sheets and Firebase (Functions). I have a Google Sheet where I pull in the news feed sections from sources such as the Google Cloud Blog and Medium. Using the built-in Sheets formulas such as IMPORTFEED and FILTER, I built a keyword-based article curation algorithm pre-selecting the articles to include in the next issue of the newsletter. Then my tool called crssnt (pronounced as the french pastry) takes the data from the Google Sheet and displays it in the newsletter. If you are curious how the Google Sheet looks like, you can check it out here.

What are your favorite Google Cloud Platform data products within the data analytics, databases, and/or AI/ML categories? What use case(s) do you most focus on in your work? What stands out about GCP’s offerings?

My favorite is BigQuery but I’m also a huge fan of Firestore. BigQuery is my tool of choice for pretty much all of my data warehouse needs (for both personal and client projects). What really stood out to me for me is the ease of use when it comes to setting up new databases from scratch and getting first results in the form of e.g. a Data Studio dashboard built on top of a BigQuery table. Similarly, I always go back to Firestore whenever I have an idea about some new front-end project since it’s super easy to get started and gives me a lot of flexibility.

From similar non-Google products, I used Snowflake a while ago but didn’t find the user interface nearly as intuitive and user-friendly as BigQuery.

What’s next for you in life? 

It’s going to be mostly ‘more of the same’ for me: as a data nerd, there is always something new to discover and learn. 

My overall message to readers would be to try to not worry too much about fitting into predefined career paths, job titles and so on, and just do your thing. There is always more than one way of doing things and reaching your goals. 

Want to join the Data Engineer Community?

Register for the Data Engineer Spotlight on July 20th, where attendees have the chance to learn from four technical how-to sessions and hear from Google Cloud Experts on the latest product innovations that can help you manage your growing data. 

Begin your own Data Hero journey

Ready to embark on your Google Cloud data adventure? Begin your own hero’s journey with GCP’s recommended learning path where you can achieve badges and certifications along the way. Join the Cloud Innovators program today to stay up to date on more data practitioner tips, tricks, and events.

If you think you have a good Data Hero story worth sharing, please let us know! We’d love to feature you in our series as well.

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