Data Engineer with a cloud-native approach across cloud, DevOps, and AI. I help engineering teams design pipelines, automate infrastructure, and ship data systems that stay up.
Trusted by teams at
Onboard client teams onto the Big Data Platform, enabling self-service adoption of Databricks, Snowflake, and AI services. Provide consultancy to internal teams to optimize workflows and automate ServiceNow incident handling.
Led the design and development of a metadata-driven ingestion framework, ingesting data from SAP, Kafka, and REST APIs into Delta Lake via Databricks, governed through Unity Catalog. Built Datadog dashboards for pipeline observability and maintained Azure Data Factory pipelines from SAP and SQL Server.
End-to-end OCR with Textract and Azure LLM. RAG-based financial chatbot. ETL pipelines with PySpark on SageMaker, EMR, and S3. Productionized ML models at scale.
End-to-end architecture on AWS with Cloudera Data Platform. Terraform, Ansible, and Vagrant for infrastructure. Presented on vector databases at company summit.
ML models for risk management. ATM failure classification in R. Expected loss models implementing IFRS 9 in collaboration with EY.
Builder. Problem-solver. Always one tweak away from a better workflow. I work where data, software, and infrastructure meet — building things that are reliable, maintainable, and pleasant to work with.
I've worked across travel tech, chemicals, fintech, consulting, and banking in Costa Rica, France, and Spain. Background in mathematics, with an MSc in Data Engineering for AI and a Master's in Mathematical Methods.
Architecture and implementation of cloud-native data platforms on AWS and Azure. From ingestion to serving, built for scale and maintainability.
Robust ETL/ELT pipelines with PySpark, Databricks, and cloud-native tooling. Schema management, retries, idempotency baked in.
Automated training, testing, and deployment pipelines. Containers, IaC, and workflow orchestration for repeatable delivery.
Metrics, logging, alerting, and SLOs for data systems. Keep pipelines healthy and catch problems before users do.
Optimize jobs, clusters, and storage layers for throughput and cost. Practical improvements, not theoretical benchmarks.
A Python library for declarative data ingestion across cloud sources. Config-driven, retry-aware, schema-validated.
End-to-end orchestration for analytics workloads on Databricks with Unity Catalog governance and automated quality checks.
Retrieval-augmented generation pipeline powering a Financial Plan creation chatbot, from document retrieval to grounded generation, for a banking data team.
Understand your objectives, constraints, team, and what success looks like.
Lean architecture and a delivery plan focused on outcomes and reliability.
Iterative delivery with CI/CD, testing, and observability from day one.
Documentation, knowledge transfer, and ongoing improvements as your needs evolve.