Resume

Tokyo, Japan

• Built a scalable, cloud-native data mesh platform on AWS and Databricks, adopted company-wide to enable governed, high-quality data sharing across domains
• Developed a multi-language Kafka ingestion SDK (Rust core with Python, Java, TypeScript, and Go bindings), deployed org-wide for real-time ingestion across heterogeneous systems
• Designed and implemented CI/CD pipelines for ML and data workflows in Databricks, integrating deployment and lifecycle tracking with MLflow
• Improved platform resilience through automated data reconciliation, OpenTelemetry instrumentation, and enforcement of data contracts
• Led development of self-service capabilities, including automated provisioning of Kafka topics, access control groups, and data product registration, reducing onboarding friction across the org
• Created platform documentation, naming conventions, and onboarding guides to support self-serve adoption by engineers, analysts, and ML practitioners

Amsterdam, Netherlands

• Managed a company-wide data platform built on Azure and Databricks, supporting large-scale batch and real-time pipelines
• Built reusable Terraform modules and automated infrastructure provisioning to reduce deployment time and enforce compliance
• Created observability tooling using Python and Kusto to ensure data quality and regulatory compliance across data products
• Implemented CI/CD with GitHub Actions and ArgoCD, automating deployment of services and pipelines to Kubernetes clusters
• Worked closely with analysts and data scientists to productionize ML pipelines and deploy feature engineering workflows

Amsterdam, Netherlands

• Migrated legacy Airflow pipelines to PySpark for scalable data processing and analytics workflows
• Optimized Docker builds and CI pipelines to cut build time and reduce cloud costs by applying multi-stage techniques
• Designed and deployed a governed data lake on S3 with robust schema management and access controls
• Collaborated with data scientists to automate training data pipelines and streamline model experimentation

Madrid, Spain

• Built ETL pipelines in Python, SQL, and Java to automate financial reporting workflows across global client accounts
• Trained and deployed ML models for forecasting and risk scoring in regulated environments
• Built and deployed full-stack data visualization tools on Linux for internal analytics teams
• Participated in project planning with clients, translating business goals into deliverable data products