Ñuñoa, Santiago Metropolitan Region, Chile
Data Engineer with experience designing and optimizing data pipelines in cloud environments, primarily on Google Cloud Platform. I specialize in building efficient and scalable solutions that transform large volumes of data into reliable, high-quality datasets for decision-making. I have worked with tools such as dbt Core for data modeling and transformations, orchestrating workflows with Apache Airflow, and developing robust pipelines using Python and Apache Beam. I have a strong focus on optimization, both in execution time and resource usage, allowing me to design balanced solutions based on business needs. Additionally, I have experience in: Designing efficient data architectures Processing large-scale datasets Integrating APIs and external systems Publishing and processing data using services like Pub/Sub and Cloud Run Applying software engineering best practices (SOLID, DRY, KISS) Ensuring data quality through automated testing and validation I am motivated to continue growing in the data engineering field, especially in modern data architectures, distributed processing, and building solutions that create real impact.
Worked with dbt Core for data modeling and transformations, orchestrated via Apache Airflow through the development and maintenance of DAGs. Designed efficient data architectures for large-scale processing, optimized database performance to reduce critical query times, implemented automated data quality tests, and documented technical workflows to improve maintainability and onboarding.