Data Engineer · Hakkoda by IBM
Snowflake platform engineering, database integration, unstructured data handling, and AI/analytics enablement for enterprise clients.
Data Engineer
I build scalable, cloud-based data pipelines for the modern enterprise.
I'm a developer passionate about building robust, scalable data infrastructure that powers business decisions. My work lies at the intersection of data engineering and cloud architecture, creating pipelines that not only process data efficiently but are built for reliability and scale.
Currently, I'm a Data Engineer at Hakkoda by IBM, specializing in Snowflake platform engineering, database integration, and AI/analytics enablement. I contribute to building data solutions that handle unstructured data and enable advanced analytics capabilities.
In the past, I've had the opportunity to work across a variety of settings — from financial technology startups to large semiconductor corporations. At Snap Finance, I led the migration of dbt models to EKS and built automated data quality monitors that reduced incidents by 30%.
When I'm not optimizing queries or designing data models, you'll find me pursuing my Master's in Big Data & Business Intelligence, exploring new cloud technologies, or enjoying the beautiful landscapes of Costa Rica.
Snowflake platform engineering, database integration, unstructured data handling, and AI/analytics enablement for enterprise clients.
Led dbt model migration to EKS, built automated data quality monitors, and reduced data incidents by 30% through proactive monitoring solutions.
Built AI demo catalog database and Power BI dashboards that reduced search time by 30% for internal teams seeking AI demonstration materials.
European Business School of Barcelona · Online
University Hispanoamericana · Heredia, Costa Rica
Python (Pandas, PySpark), SQL, JavaScript
dbt, Trino, Star Schema
Apache Airflow, GitHub Actions
AWS (S3, EC2, EKS), Azure, Docker, Kubernetes
SQL Server, PostgreSQL, MySQL, Oracle, Snowflake
A comprehensive guide to dbt development patterns, testing strategies, and project organization for scalable analytics engineering.
Deep dive into Snowflake query optimization, warehouse sizing strategies, and cost management techniques for enterprise workloads.
Exploring machine learning approaches to automated data quality monitoring and anomaly detection in data pipelines.
Practical guide to deploying and managing data transformation workloads on Amazon EKS with cost optimization strategies.