CHRIS LUTTAZI
Lead Data Engineer. I build lakehouse platforms on Databricks & Spark, model with Data Vault 2.0, and make data AI-ready — 10+ years, 4 continents, based in Tokyo.
Lead Data Engineer specialising in large-scale lakehouse platforms built on Databricks, Apache Spark, Delta Lake and Apache Iceberg, with deep expertise in Data Vault 2.0 modelling, real-time streaming and cloud data warehousing. I design AI-ready data platforms — governed, high-quality data foundations that power analytics, machine learning, RAG pipelines and vector search — and I work AI-augmented, pairing daily with Claude and GitHub Copilot to ship production-grade systems faster. 10+ years across fintech, e-commerce, energy and enterprise, from Scandinavia to Japan.
Chris Luttazi is a Lead Data Engineer specialising in large-scale data platforms built on Databricks, Apache Spark, and the modern lakehouse ecosystem.
His toolkit covers the full modern data stack: lakehouse architecture on Delta Lake and Apache Iceberg, Data Vault 2.0 and dimensional modelling, streaming and CDC pipelines with Kafka and Spark Structured Streaming, orchestration and transformation with Airflow and dbt, and governance with Unity Catalog and data contracts.
Since the rise of generative AI, Chris has focused on AI-ready data platforms — the governed, high-quality data foundations that LLM applications, RAG systems and vector search depend on — and on AI-augmented engineering, using Claude and GitHub Copilot daily to design, build and test pipelines faster.
With experience spanning fintech, energy, e-commerce, and enterprise consulting, Chris has delivered data solutions across four continents — from Scandinavia to Japan. Currently based in Tokyo, Japan.
▌ Azure Data Engineer Certified
Available for data engineering engagements, platform architecture, AI data-platform builds and consulting across fintech, e-commerce and regulated industries.