Description

PROFESSIONAL SUMMARY

Dynamic leader with over 15 years of experience driving digital transformation through data and AI innovation. I specialise in architecting and deploying scalable, cloud-native solutions that align technology strategies with business objectives. Throughout my career, I have led high-performing teams across multiple industries (including finance, transportation, automotive, and insurance) successfully migrating legacy systems to modern cloud platforms and establishing robust data mesh architectures.

My expertise in leveraging AWS, GCP, and Hadoop ecosystems has enabled organisations to unlock actionable insights, optimise operational agility, and achieve competitive advantages. I excel at translating complex technical challenges into clear, business-driven outcomes, fostering collaboration between cross-functional teams, and implementing advanced analytics and AI solutions that empower decision-making at every level.

KEY SKILLS AND COMPETENCES

Strategic Leadership & Team Management: Proven ability to lead and inspire cross-functional teams, driving digital transformation and aligning technology initiatives with overarching business objectives.

Cloud Architecture & Data Modernisation: Expertise in deploying scalable, cloud-native solutions on AWS and GCP, migrating legacy systems, and establishing robust data mesh architectures to support growth.

Advanced Analytics & AI/ML Integration: Skilled in leveraging cutting-edge AI and machine learning technologies to deliver real-time insights, predictive analytics, and generative AI capabilities that enhance business decision-making.

Efficient Data Pipeline Development: Strong background in engineering end-to-end data ingestion, integration, and processing pipelines using technologies like Apache Spark, Apache Beam, and DBT.

Languages

English
Native or bilingual
French
Native or bilingual
Arabic
Native or bilingual

Workplace preferences

Can work on-site

Dubai City (up to 50km), Abu Dhabi (up to 50km)

RATPDEV
Data & AI Solutions Architect
February 2024 - Today (2 years and 4 months)
Paris, France
Overview:

Led the design and deployment of a state-of-the-art data and AI platform built on AWS Cloud and integrated with Snowflake.

Key Responsibilities:

=> AWS Cloud Architecture & Implementation:
Architected and deployed the platform on AWS, leveraging key services such as Airflow, S3, Lambda, Managed Kafka, and Managed Flink to ensure a secure, scalable, and cost-effective infrastructure.
Optimised cloud resources to support high-volume data processing and real-time analytics for AI and ML applications.

=> Snowflake Integration & Data Processing:
Designed and implemented robust data pipelines for efficient ingestion, transformation, and storage of both structured and unstructured data.
Integrated Snowflake's cloud data warehouse to streamline data storage, accelerate query performance, and enable real-time analytics for dynamic AI/ML use cases.

=> Advanced Data Modelling & Analytics:
• Developed sophisticated data models using DBT tailored to support AI and ML workloads, ensuring data accuracy and integrity through best practices in normalisation, dimensional modelling, and OBT schema design.
• Enhanced data processing workflows to facilitate timely and actionable insights, driving predictive analytics and decision-making.

=> AI/ML Integration & Optimisation:

Integrated cuting-edge AI and ML inference modules to deliver real-time predictive capabilities and advanced analytical insights.
Continuously refined data pipelines and analytical models to improve performance, reliability, and scalability across multiple regions.

=> Cross-functional Collaboration & Leadership:
Collaborated with data engineers, ML specialists, and business analysts to align technical solutions with strategic business objectives.
Managed project timelines, resource allocation, and stakeholder expectations to ensure successful and timely delivery of platform enhancements.
BPI France
Data Architect
January 2022 - February 2024 (2 years and 1 month)
Paris, France
Overview:

Pioneered the deployment of cloud-native data solutions on AWS, building a scalable data mesh and enabling both batch and real-time pipelines for critical analytics and Generative AI initiatives.

Key Responsibilities:

=> AWS Cloud & Data Migration:
Planned and executed large-scale data migration to AWS, employing Spark, Managed Kafka, Managed Flink and DBT for streamlined data ingestion, transformation, and integration.
Devised robust Kimball and OBT data models to optimise data accessibility, supporting both traditional BI analytics and real-time AI pipelines.
=> Kubernetes-Based Data Mesh:
Built and maintained a data mesh on Amazon EKS, orchestrating both batch and real-time processing.
Employed Apache Iceberg on Amazon S3 for scalable, version-controlled storage, facilitating efficient data updates and historical tracking.
Incorporated HPC cluster scaling and BFS libraries for distributed computing, enabling high-performance AI and ML workloads.

=> Generative AI Enablement:
Integrated Generative AI workflows using open-source MLOps frameworks (Kubeflow, MLflow, Hugging Face) on Amazon EKS.
Oversaw foundation model training, fine-tuning, and real-time inference pipelines, including user feedback loops for continuous model improvement.
Ensured comprehensive model governance, from experimentation to production deployment, aligning with security and compliance standards.

=> Real-Time & Batch Analytics:
Deployed and optimised Spark-based streaming for real-time analytics, enabling quick insights for AI driven decision-making.
Designed and maintained batch processing flows for large-scale data transformations, guaranteeing consistent data availability and quality.
=> Cross-Functional Collaboration & Leadership:
Partnered with data scientists, DevOps engineers, and business stakeholders to align data strategies with evolving organisational needs.
RENAULT GROUP
Lead Data Engineer
June 2019 - December 2021 (2 years and 6 months)
Paris, France
Overview:

Orchestrated the end-to-end modernisation of legacy Hadoop workflows onto Google Cloud Platform (GCP), establishing a data mesh architecture that powered data ingestion, integration, migration, and real-time analytics for over 230 projects. Leveraged PubSub, Dataproc (Apache PySpark), Dataflow (Apache Beam), and DBT to create Kimball and OBT data models in BigQuery, ensuring seamless support for both business analysis and real-time front-end applications.

Key Responsibilities:

=> Data Mesh Implementation & Migration:
Designed and deployed a data mesh on GCP to unify disparate data sources, simplifying cross-functional collaboration and data reusability.
Led the migration of critical data assets from on-premise Hadoop clusters to BigQuery, ensuring minimal disruption to ongoing operations.

=> Cloud-Native Data Processing:
Engineered large-scale batch and real-time data pipelines using Dataproc (Apache PySpark), Dataflow (Apache Beam) and GCP PubSub for robust, low-latency processing.
Optimised data ingestion workflows to handle diverse data formats and volumes, improving reliability and scalability.

=> Data Modeling & Transformation:
Developed Kimball and OBT data models in BigQuery using DBT, enabling streamlined data transformations and efficient reporting.
Ensured data quality, consistency, and performance across a wide array of analytics and real-time UCs.

=> Cross-Functional Collaboration & Governance:
Collaborated with data scientists, business analysts, and platform teams to align data strategies with evolving project needs and corporate objectives.
Instituted data governance best practices, security protocols, and CI/CD pipelines to maintain high standards of data integrity and compliance.