2026 — Present
Data Platform Engineer / Amadeus
Data platform engineering for one of the world's largest travel technology companies. Cloud-native infrastructure and data systems at scale.
I build and operate
reliable data platforms
Data Platform Engineer with a cloud-native approach. I help engineering teams design pipelines, automate infrastructure, and ship data systems that stay up.
Trusted by teams at
- Amadeus
- BASF
- BBVA
- Adaltas
- Banco Nacional CR
Experience
2025 — 2026
Senior Cloud Data Software Engineer / BASF
Data distribution strategies with Unity Catalog, Azure Data Factory, and Databricks. Built a metadata-driven ingestion framework for cross-team data sharing.
2023 — 2025
Machine Learning Engineer / BBVA AI Factory
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.
2022 — 2023
Big Data Engineer / Adaltas, Paris
End-to-end architecture on AWS with Cloudera Data Platform. Terraform, Ansible, and Vagrant for infrastructure. Presented on vector databases at company summit.
2020 — 2022
Mathematical Models Developer / Banco Nacional de Costa Rica
ML models for risk management. ATM failure classification in R. Expected loss models implementing IFRS 9 in collaboration with EY.
About
Tinkerer. Builder. 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.
Approach
- Pragmatic engineering over complexity
- Reliability and observability first
- Strong collaboration with product and platform teams
Education
- MSc Data Engineering for AI — DSTI
- BSc Mathematics — UCR
Certifications
Languages
- Spanish — native
- English — fluent
- French — professional working
Services
Data platform design
Architecture and implementation of cloud-native data platforms on AWS and Azure. From ingestion to serving, built for scale and maintainability.
ETL & pipeline engineering
Robust ETL/ELT pipelines with PySpark, Databricks, and cloud-native tooling. Schema management, retries, idempotency baked in.
MLOps & CI/CD
Automated training, testing, and deployment pipelines. Containers, IaC, and workflow orchestration for repeatable delivery.
Observability & reliability
Metrics, logging, alerting, and SLOs for data systems. Keep pipelines healthy and catch problems before users do.
Cost & performance tuning
Optimize jobs, clusters, and storage layers for throughput and cost. Practical improvements, not theoretical benchmarks.
Tech stack
Languages
- Python
- SQL
- Bash
- Docker
Platforms
- AWS (SageMaker, EMR, S3)
- Azure (Databricks, ADF)
- Databricks / Unity Catalog
Infrastructure
- Docker, Kubernetes
- Terraform HCP, Ansible
- GitHub Actions
Data & ML
- PySpark
- ETL / ELT pipelines
- Model serving & monitoring
Selected work
Metadata-driven ingestion framework
A Python library for declarative data ingestion across cloud sources. Config-driven, retry-aware, schema-validated.
Databricks workflow orchestration
End-to-end orchestration for analytics workloads on Databricks with Unity Catalog governance and automated quality checks.
Pipeline observability platform
Monitoring and alerting layer for production data pipelines. Metrics, log aggregation, and SLO dashboards for a fintech data team.
How I work
-
01
Discovery
Understand your objectives, constraints, team, and what success looks like.
-
02
Technical design
Lean architecture and a delivery plan focused on outcomes and reliability.
-
03
Build
Iterative delivery with CI/CD, testing, and observability from day one.
-
04
Handover & support
Documentation, knowledge transfer, and ongoing improvements as your needs evolve.