10 years of experience, including 7 years leading data & tech teams of up to 35 people. I help organizations design, industrialize and govern their data platforms and AI solutions — while keeping my hands in the code.
Leadership, architecture or delivery: the scope adapts to your needs. In every case, I remain hands-on — from POC to production code.
Department or program leadership: data-driven strategy, team structuring and upskilling, data governance, change management and alignment of metrics with business objectives.
Design of cloud data platforms and application solutions: Data Mesh, medallion architecture, real-time and event-driven integration, architecture documentation and cost / performance / security trade-offs.
From ML models in production to AI agents: prediction, optimization, LLM & RAG, OCR, business process automation. Full industrialization — CI/CD, A/B testing, monitoring.
A selection of projects led from design to production. Company names are anonymized for confidentiality — details available on request.
Design and deployment of an ML system (Random Forest, polynomial regression) predicting fiber-network failures, coupled with route optimization (KNN). Production deployment via CI/CD, A/B testing on models, automated scheduling.
Cloud architecture structured as a Data Mesh: domain data ownership, data products, data catalog and a governance framework (quality, lineage, documentation, testing). Azure stack: Snowflake, dbt, Databricks, Airflow — Bronze/Silver/Gold medallion architecture.
Reverse-engineering of existing financial calculation rules, then from-scratch design of a mapping engine in Python/FastAPI: ERP ingestion, vectorized consolidation computations and real-time delivery. dbt/Snowflake pipelines, Docker-standardized environments.
Design and development of a SaaS ERP built on a pool of AI agents: HR system, domain-based activity management, project tracking, planning assistant and resume analysis. Fully self-hosted infrastructure: CI/CD, Docker, server hardening.
Every engagement follows the same logic as a data pipeline: clear, tested stages that only ship downstream what has been validated upstream.
Needs audit, field listening, mapping of the existing landscape and business pain points.
discovery · double diamondTarget architecture, technology trade-offs, governance and migration trajectory.
c4 model · well-architectedFocused POC on the highest-value use case, with measurable success criteria. Output: a go/no-go decision — ROI, compliance, build vs buy.
mvp · a/b testing · go/no-goCI/CD, testing, data quality, documentation: from prototype to reliable product.
dataops · mlopsTeam upskilling, rituals, autonomy — the value stays with you.
adkar · mentoringCompanies are anonymized — references and details available in direct conversation.
Deliberate technical assignment: real-time financial consolidation engine, dbt/Snowflake pipelines, governance standards and coordination of external teams.
End-to-end leadership of a data & AI activity: team of 6, AI-agent SaaS ERP, fully self-hosted infrastructure.
Department of 35 people across 4 units, ~€2M budget. ML in production, smart applications, Data Mesh architecture and a unified group data platform.
Built the Data unit from scratch: team of 16, on-premise architecture (data lake, DWH, data marts), governance, data evangelization across operational units.
Python/R/VBA development, report automation, business simulations, BI dashboards and analytics project management.
Université Paris-Dauphine · MBA Erasmus exchange (Vienna)
French (bilingual) · Arabic (native) · English (professional)
Available for new opportunities — data leadership, solution architecture, digital transformation or high-value AI solution design. Based in the Paris area, working across France and internationally.