Albert Sugranyes
Notes on data-intensive systems, performance, and real-world scale.
Systems are shaped by their constraints.
Albert works on the architectural requirements necessary for data and AI systems to operate reliably at scale.
Correctness is assumed. The real question is whether a system remains understandable, governable, and efficient once it meets reality.
Over the past years, I have focused on designing and operating distributed systems. I currently lead distributed data processing at Airbus. In parallel, I contribute to open-source large-scale data systems, including Apache Spark. I am now expanding into AI systems and inference efficiency.
Senior software engineer with staff-level ownership of distributed systems.
I define execution architectures for data-intensive systems across the configuration management domain, spanning compute infrastructure design, resource orchestration, and deployment models.
I provide architectural direction for large-scale data processing platforms, guiding how teams design, operate, and evolve distributed pipelines and storage systems supporting multiple aircraft programs (A320, A330, A350, A380, A400M).
I worked on large-scale data processing workflows with Apache Spark on Databricks.
I defined and implemented production-grade data pipelines, and contributed to the architecture of the company’s lakehouse platform.
I developed deep expertise in distributed data systems, with a strong focus on Apache Spark performance optimization and efficient resource management.
BSc in Mathematics, with a specialization in computer science and artificial intelligence. This is where I developed a strong interest in computation and learning-based systems, culminating in a final project on interpretability methods in deep learning.
Notes on complex software systems.