Meet Črtomir! During his studies, he developed a strong interest in data and distributed systems. After finishing his MSc, he always pursued interesting roles to gather comprehensive knowledge and experience in “Everything Data”. Before BlueLabs, he worked as a Lead Engineer for an Insurtech company, focusing on processing large datasets, machine learning, natural language processing, and search systems.
Črtomir first heard about BlueLabs from a colleague that previously worked here. He went through the interview process and liked the company's direction, highly qualified people, and technical setup, together with the opportunity to work on a greenhouse project. As there was no data team before him, he was excited by having the ability to shape the Data Platform from the ground up. With no previous experience in sports betting, the journey began with a lot of new challenges that required obtaining domain knowledge, performing research, thorough planning, and picking the right tech. This led to building foundations and delivering the first production-ready version of the platform, which was able to deliver timely insights to the business.
His role evolved from being very hands-on to focusing more on coordination, technical guidance, and team development. He wants his team to feel empowered and be able to impact and own part of the product. As an Engineering Lead, he still maintains very much hands-on by building stream-processing pipelines, data transformations, workflow automation, and self-serving business analytics. As well as turning business requirements into a technical plan and collaborating with other teams on system design.
He’s driven by having talented colleagues, empowering working environment, well-architected technical solutions, and lots of tough challenges. Seeing how teams continuously deliver value for the company while maintaining high standards for quality and technical execution, fills him with delight.
The team is currently working on integration with newly added internal services and external systems required by the new client. The current deadline is also focused on performance improvements, multi-tenancy, expansion of the data warehouse, and data analysis capabilities.
There’s a lot of potential in the data acquired, which can bring a ton of added business value. Over the next year or so, he believes that there'll be room to focus more on predictive analytics and ML to automate and improve some of the tasks like detecting risk, fraud detection, and advanced user segmentation. In-depth exploration and understanding of user behavior data can drive promotions and improve user retention.