Summary
Daniel Costa is a data engineer with 9 years of experience building scalable data pipelines and cloud migrations across GCP, Azure, AWS and Snowflake for clients in finance, industry and research. He has delivered end-to-end solutions—from web scraping and ETL with Spark and Databricks to transforming Dynamics 365 into Azure Data Lakes and re-architecting legacy APIs—frequently improving performance and reducing costs. His research background at INESC produced published work on adaptive middleware for IoT data lakes, highlighting a focus on low-latency, bandwidth-efficient architectures for constrained networks. Comfortable across full-stack and cloud-native stacks, he pairs practical engineering with a collaborative, knowledge-sharing approach. Currently at Tiko, he continues to drive cloud migrations and reporting platforms informed by hands-on experience in production and academic settings. Outside work he sharpens problem-solving skills with Rubik’s cubes, a small but telling indicator of his interest in algorithmic thinking and optimization.
9 years of coding experience
5 years of employment as a software developer
Informatics Engineering Computer Science and Informatics, Informatics Engineering Computer Science and Informatics at Escola de Engenharia da Universidade do Minho
Ensino Secundário Ciências e Tecnologia, Ensino Secundário Ciências e Tecnologia at Colégio Liceal de Santa Maria de Lamas