Ghiles Babou

**Invoice Project**: Supervision and reliability of the internal invoice lifecycle, from creation to accounting recognition, to identify, analyze, and facilitate the resolution of billing process blockages.

• Design of the project's technical architecture, based on an event-driven approach, to model and track the invoice lifecycle.
• Implementation of end-to-end data pipelines utilizing event streams for ingestion, transformation, and consolidation of multi-source data.
• Implementation of enrichment and contextualization mechanisms for events based on the billing stage and source system.
• Definition and maintenance of log retention and archiving policies according to volume, performance, and cost constraints.
• Development of dedicated decision-making dashboards for billing incident management.
• Delivery of projects across all environments, from design to production, with technical and user documentation for the dashboard operation teams.

**Technical Environment**: Elastic Stack, Ingest Pipelines, Kafka, Docker, CentOS, Azure DevOps, GitFlow.

**Inbound / Outbound**: Within L'Oréal's OPS & FIN teams, contributed to operational steering data projects for inbound/outbound logistics flows (supply chain), aiming to ensure data reliability, track operations, and support business decision-making.**Business Partners / Product**: In the same department, worked on projects dedicated to business partner and product master data to structure, validate, and make reference data usable for business and finance teams.

• Collaboration with business and IT teams to translate functional needs into technical solutions.
• Delivery of real-time pipelines for ingestion, transformation, and consolidation of multi-source data in inbound and outbound processes.
• Batch processing and transformation of data, including parsing, cleaning, and enrichment for business partner and product domains.
• Implementation of normalization, multi-source correlation, and business indicator/status calculation rules to create consolidated datasets for reporting and operational analysis.
• Creation of dashboards for flow monitoring, incident analysis, and system intervention.
• Management of deployment and production release in an end-to-end delivery process with post-production support.

**Technical Environment**: Elastic Stack, Ingest Pipelines, Kafka, Spark, CentOS, Azure DevOps, GitFlow.

**LaCipav Project**: For La Cipav, worked on a real-time data processing project to ensure reliable event circulation and improve data availability for operational and IT uses.

• Creation of a data streaming processing platform based on the Kafka ecosystem.
• Configuration of Kafka Connect (sources & sinks) for critical data synchronization.
• Design of Kafka Streams topologies for streaming and structuring data flows.
• Development of microservices for event collection, transformation, and distribution.
• Contribution to the evolution of a scalable, robust, and maintainable architecture for new use cases.

**Technical Environment**: Apache Kafka, Kafka Connect, Kafka Streams, GitLab, Docker, Linux.