Summary
Daniel Amariles is a data engineering and AI practitioner with 11 years of experience building cloud-native, scalable data platforms and teaching advanced cloud data processing for a masters program. He blends hands-on expertise in Spark, Hadoop, Snowflake, ElasticSearch and LLM/ML tooling with strong AWS and GCP proficiency (including Bedrock, Redshift, Glue and BigQuery) to deliver production ML and data workflows. Currently splitting time between senior data engineering and Data & AI software design at Globant and lecturing at Universidad ICESI, he excels at translating research-stage solutions into operational pipelines. His background leading biodiversity data integration and GBIF coordination reveals a knack for complex, messy datasets and cross-organizational collaboration. Colleagues value him for pragmatic MLOps patterns and a track record of shipping resilient, observable systems in both research and enterprise contexts.
11 years of coding experience
4 years of employment as a software developer
Universidad de los Andes
Engineer's degree Software Engineering, Engineer's degree Software Engineering at Universidad Icesi
Course German (Deutsch) A1, Course German (Deutsch) A1 at Sprach Institut
UOC (Universitat Oberta de Catalunya)
Spanish, English, German