Meet The Graduates: Guoda Paulikaite

Just recently I joined a company that was represented by one of the guest speakers at the bootcamp, and that has really given a boost to my career in this field. Now that I'm at Project A Ventures, I am absolutely having a blast, enjoying the work I do and, yeah... I mean… earlier I've used to have a very clear answer to your question, but now I think I'm at the beginning of something new where I'm not only a data analyst or a business analyst. At the moment I am a data analyst that supports many startups and ventures in their requests for data analytics topics. I am an analyst who works on solving data problems to deliver actionable insights for a variety of business goals. But also shaping their strategies of how they're going to scale and what data needs they will have in data analytics, structures or architectures. 

P: So if I understand this correctly, you combine multiple skills in your current job: you are a data analyst, a data engineer, a data architect, and you're also involved in the business- and organisational development of various ventures, right?

G: Yes, indeed. Actually, data analyst sounds like I'm given data, I need to analyse it and that's pretty much it. But there is so much more that needs to happen before. And it seems that in the field of data there are many titles being created just to follow up with all new emerging required skills. So this is why I love my organisation for not worrying about this and just keep on focusing on the actual effort required. Yeah, leaving the content behind the titles flexible.

So, you're totally right. I'm part of managing projects, advising ventures on their high-level data architectures, investigating really specific technical solutions, thinking about processes, thinking about blockers. After all the goal is to analyse the data in a way that it's actionable, but it's just sort of the peak of the mountain. And I take a big part of what's underneath, working together with data engineers and different departments internally and externally to get to that that peak. 

P: What was your intention behind joining the data engineering course?

G: Previously having worked in IT teams of large companies, my expectation for joining was to gain the confidence that I've always felt some lack of, you know, coming from a different academic background, even if I was very interested in data and I was good at pushing IT projects forward. I had this idea that maybe I could just go ahead and learn something in a non-academic, non-traditional format with more recent technologies. 

I really had a good idea from your marketing materials that it will be a course covering many different data fields. Actually, this was my highest wish and expectation that I will get a good understanding on all of them to be comfortable in dealing with different architectures, different situations, different projects without going into extreme depth or getting lost in details of just one field, but having that grand overview.

P: How did three months of learning online feel for you? 

G: I would say, I would have preferred a physical classroom much more, but it went much better than I expected, and this was I think due to the group size that we’ve had (P: cohort of 9 individuals). The fear that I've had about the online classroom was that I won't get enough feedback, or no one will notice my progress or when I'm struggling. Or, you know, just miss out on having fun building relationships with your peers. 

But dealing with this was so similar to how you go about it in the real world: your colleagues will not be around all the time, they will be somewhere else geographically and you have to figure out how to talk to them effectively. 

P: If you have to think back, it’s already been 10 months since you've graduated. What are your most memorable moments? Highlights, lowlights, good things, bad things from the course. 

G: I do remember that the course was very challenging. It wasn't something where you sit back and relax and just get information stuffed in your head. It was many times so challenging that I was just asking myself how can I go forward. One of the highlights was how Daniel actually spotted this frustration and was able to manage it and bring me to take one step back together and say okay, this is all gonna be fine. It sounds quite straightforward and simple, but actually it's one of the things that you learn that the level of detail can be so overwhelming that it's hard to avoid getting lost. I think this is one of the skills that I've learned.

The amount of information and amount of complexity makes it difficult for you to grasp how the world can work, how planes can fly and bank transactions can run if everything's so complex. Then I realised that no one actually has a simple answer. It's just a matter of experience and willingness. You know, not to get lost and just continue. Fail and stand up again. And when I realised that everyone is doing the same, like everyone, even the most experienced developers, they do the same thing. They just go on and search for a solution.

P: Did your perception of data engineering, or the role of a data engineer change throughout or after the course?

G: It became much more down-to-earth than what I imagined before. I imagined that you really need to have a degree in computer science. I still consider that people who make that choice to study computer science get a really good advantage, a good basis. But after all, it's all about experience and curiosity — and if you have that, then the rewards are great.

P: Where do you think your career is headed right now? 

G: I'm headed to a place where I'm confident with the uncertainty with different architectures that I've never seen before. There's always new emerging architectures, new technologies. Now I am able to have a conversation about them with confidence, and a structured way of approaching implementing them in projects, and managing the stakeholders along the way.

P: Confidence in uncertainty. I love this expression. Data engineers need a lot of this.

There are people out there who are considering embarking on this path of learning data engineering. Do you have an advice for them?

G: I have so many different tips that I could share. Some of them are more specific, some are more about not losing motivation and persistence. But overall, I think my number one tip would be to try to find a problem that you think you can solve, to have one project that you can work on throughout the whole academy. A tangible, small problem you want to solve, not an abstract, academic one. That’s a great starting point.

P: Thank you, Guoda.