Description
Discover how Novacomp helped a global fintech accelerate its growth by structuring and deploying a multidisciplinary Data and Artificial Intelligence team. Through a rigorous process of attracting and integrating specialized talent, this solution enabled the restructuring of the data architecture, optimization of Machine Learning models, and reduction of technical debt to its lowest recorded level in less than a month of operation.
What will you discover in this document?
By reading this case study, you will learn:
- The challenge of scaling and accumulated technical debt: The operational difficulties of a fast-growing fintech whose internal team could not keep up with platform demands, compounded by the lack of a clear structure for data engineering responsibilities across its analytics areas.
- The methodological approach and specialized team assembly: The execution of a comprehensive selection process (live technical evaluations, practical tests, and cultural fit and English proficiency validations) to build a high-performance team comprising a CTO/Tech Lead, Data Engineers, Machine Learning Engineers, Data Analysts, and QA Architects.
- The cutting-edge tech stack and infrastructure: The use of cloud environments with AWS, orchestration in Docker and Kubernetes, SQL and NoSQL databases (MySQL, Neo4j), data warehousing in Snowflake, Python AI frameworks (Pandas, Scikit-learn), web development in ReactJS, Java/Scala, and the Fineract core banking platform.
- The results in technical impact and development velocity: The formal creation of the Data Engineering department, standardization of data pipelines, optimization of predictive models, and the deployment of a quality framework with high code coverage across all development teams.