Loïc Bélec

Cloud & Data Solutions Architect – Technical Manager

I lead the design and operation of Alpine Racing’s hybrid data infrastructure, combining on-premise systems with Microsoft Azure to support real-time analytics, manufacturing operations, and advanced engineering workflows in a high-performance environment (Formula 1, Le Mans).

My responsibilities include defining enterprise cloud strategy, ensuring architectural alignment with business and regulatory imperatives, and enabling seamless data integration across SAP, 3DEXPERIENCE, telemetry systems, and other legacy databases - orchestrated via Azure Data Factory and modular pipelines.

I’ve also initiated exploratory work around GCP workload migration, to assess the viability of a multi-cloud strategy in line with Alpine’s long-term platform objectives.

I manage a team of six (cloud architects, data engineers, DevOps specialists) and act as technical lead, with responsibilities including:

• Designed a centralized data lake aligned with enterprise architecture principles (TOGAF, Azure Well-Architected Framework), and implemented a secure hub-and-spoke topology with Express Route for high-throughput, low-latency connectivity.
• Operate a dual environment (on-prem + Azure), integrating DevSecOps, FinOps, and GDPR controls. Defined governance structures in coordination with security, compliance, and IT stakeholders.
• Standardized cloud environments using Terraform, Helm, GitOps, and Azure DevOps. Automated deployments and embedded policy-as-code across the stack.
• Built modular ingestion pipelines with Azure Data Factory, integrating dbt for transformation workflows and data contract enforcement. Enabled versioning, SLA tracking, and testable models for critical business datasets.
• Built a semantic knowledge graph connected to the enterprise data lake, and developed a LLM-powered RAG system to enable engineers to perform natural language queries across business and technical datasets.
• Acted as architecture referent for technical boards and strategic programs driven by the executive committee. Promoted modern cloud-native and data mesh principles, enabling cross-functional alignment around sustainable, scalable designs.

High-Performance Data Platform Architect

I led the architecture and deployment of a high-frequency data platform based on kdb+ and q, technologies originally developed for real-time trading. The platform aggregated data from track telemetry, test benches, wind tunnel, and simulator, enabling engineering teams to monitor and analyze performance in real time.

To build this system, I conducted a detailed benchmark of various technologies (IBM Watson, InfluxDB, Azure Data Explorer, and others), ultimately selecting kdb+ for its performance and scalability. I was responsible for the entire Azure infrastructure — development, test, and production environments — and implemented infrastructure-as-code using Terraform.

I also managed Kafka integration across the two factories and at the track to ensure low-latency data streams. On the modeling side, I co-defined the data architecture to reflect engine-specific constraints, collaborating with my UK counterpart who led the chassis/aero scope.

As the technical lead, I translated engineering requirements into data specifications, drawing on my earlier experience as a data engineer within the design office. Finally, I took charge of FinOps, aligning cloud cost structures with the seasonal and operational rhythms of the team.

Data Engineer

At Renault Sport Racing, I engineered robust ETL pipelines to consolidate data from CFD simulations, track telemetry, and engine test benches, laying the groundwork for a more unified data infrastructure.

I developed a modular MATLAB framework tailored for real-time analysis in race environments, and standardized processing workflows to build a stronger data culture among engineers. By integrating SAP, SQL databases, and Atlas, I enabled predictive modeling of engine components, with a strong focus on energy efficiency and actionable performance insights.

I also led the automation of performance reporting for Grand Prix weekends, accelerating insight generation and supporting strategic decision-making under pressure.

Researcher – Master’s Project

As part of my research at Tongji University, I focused on U-shaped assembly line balancing (U-ALBP), exploring both theoretical models and industrial applications.

I implemented and benchmarked optimization algorithms —including Ant Colony Optimization— in MATLAB, conducting comparative case studies to evaluate performance across different manufacturing scenarios.

This work led to a peer-reviewed publication at the 21st International Conference on Industrial Engineering and Engineering Management.

University of Tongji, Shanghai