Becoming a Databricks Champion is not simply about earning another credential. It is a demanding professional journey designed to validate something much broader: the ability to translate complex business challenges into well-designed technical solutions, apply sound software development and data architecture principles, and defend the architectural decisions behind them.
We spoke with Oleksii Zarembovskyi, who has completed all the stages and earned Databricks Champion recognition, about what the journey involves, what makes it challenging, and what it reveals about the skills expected from experienced data architects — and, ultimately, the kind of thinking that matters when designing data platforms for real-world business needs.
Background & experience:
Over 6 years of experience in the data engineering field. Oleksii leads a team of 15+ Data Engineers, with a focus on organisational and technical debt coverage, performance improvements, and best practices implementation.
What motivated you to pursue the Databricks Champion path?
Oleksii: For me, the value goes beyond the title itself. The process is an opportunity to demonstrate not only technical knowledge of Databricks, but also the ability to approach complex business problems from an architectural and consulting perspective.
It requires substantial hands-on experience. You need to be able to show what you have built, explain why particular architectural decisions were made, and critically assess your own solutions.
How is this different from a traditional technical certification?
Oleksii: A certification validates a certain level of knowledge, but this process goes considerably further.
You are expected to demonstrate real-world experience and show how you have applied your expertise to actual business challenges. It is not enough to know how the technology works. You need to explain why a particular solution was appropriate, what alternatives you considered, what trade-offs were involved, and how the architecture could potentially be improved.
That makes the process much closer to demonstrating architect-level expertise than preparing for a conventional exam.
For a data platform, this kind of thinking matters because architectural decisions can have long-term implications for performance, scalability, maintainability, and cost.
Are there any prerequisites to be eligible to receive Databricks Champion status?
Oleksii: Actually, there are a bunch of them. First of all, only Databricks partners can nominate you for the status of Databricks Champion.
Once you are nominated, you have to have at least 2 valid certificates from Databricks; one of them should be at the Professional level. Due to my Data Engineering background, I took the Data Engineer Associate and Professional certifications.
After that, you have to participate in a 3-day training from Databricks that will give you a high-level overview of the Databricks Platform and its latest capabilities. Basically, it's preparation for the board review that you will have at the end.
And finally, you'll have a board review by Databricks representatives that will evaluate your professional knowledge of Databricks, soft and hard skills.
What does the board review look like?
Oleksii: So, for the board review, you have to present the real Databricks use cases that are already productionized or in the development phase. For the board review, you have to have at least 2-3 real cases, though I prepared 6 of them.
My Databricks mentor and I decided to submit all six rather than selecting only the most impressive examples. The cases represented different types of challenges, including business process automation, data extraction and movement, trading-related solutions, and lambda architecture implementations.
Each case needed to demonstrate not only what was delivered, but also the reasoning behind the solution.
How did the case review go?
Oleksii: This is where I presented all six of my Databricks use cases to Databricks representatives.
During the presentation, you have to not only explain the architecture, but also justify your decisions and demonstrate that you understand the limitations of your own solution. You should be prepared to discuss questions such as:
- What would you change today?
- Where are the potential bottlenecks?
- What alternatives could have been used?
- How would the architecture behave at a different scale?
In other words, the goal is not to present a solution as perfect. It is to demonstrate that you understand it deeply enough to challenge your own decisions.
That is also an important part of designing solutions for clients: understanding not only how an architecture works today, but how it may behave when the volume, requirements, or business context changes.
What do you think is the most important skill being assessed?
Oleksii: Technical expertise is obviously essential, but I think the process also tests your ability to connect technology with business needs.
At the architect level, knowing individual Databricks features is only part of the job. You also need to understand how those technologies fit into broader software development, data engineering, and cloud architecture practices.
You also need to be able to communicate those decisions clearly both to technical specialists and to stakeholders who may not have the same technical background.
What has been the biggest takeaway from the journey so far?
Oleksii: One of the most valuable aspects has been revisiting projects that I already considered completed and looking at them from a different perspective.
When you know that experienced specialists are going to challenge your architectural decisions, you start asking yourself different questions:
- Why did we choose this approach?
- Would I make the same decision now?
- What could be simplified?
- What would I redesign?
That kind of reflection is valuable regardless of the final title. It makes you a stronger architect.
It also reinforces something that is easy to overlook when delivering a project: a solution being successfully implemented does not necessarily mean it is the best solution for every future scenario. Revisiting those decisions helps identify what should be preserved, what could be improved, and where an architecture may eventually need to evolve.
What comes next?
Oleksii: Having successfully passed the review of all six cases, the focus now is on preparing to defend the architectural decisions behind them and demonstrate the depth of experience accumulated across those projects.
Successfully completing this stage would mark the culmination of a journey built on years of hands-on work with complex data challenges — and open the door to deeper engagement with the broader Databricks expert community.
At the architect level, it is not enough to know how to build a solution. You need to understand why you built it that way, what its limitations are, and what you would change if the context changed.
The path toward Databricks Champion reflects a broader principle of senior technical expertise: technology knowledge matters, but the ability to make, explain and critically evaluate architectural decisions is what turns technical experience into trusted advisory expertise.
What does this journey reveal about data architecture?
Oleksii: For clients, that perspective matters when choosing an approach for a new data platform or evolving an existing one. The important question is not only whether a technology can solve today's requirements, but whether the architectural decisions behind it make sense for the business context, expected scale, and future needs.
Strong data architecture is about understanding the decisions behind it well enough to challenge, explain, and evolve it when the business requires it.
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