Mohamed Chedly Essid

• Contribute to the Trusted Data Fabric (CTDF), a global data platform supporting analytics and data operations across 30+ countries, enabling self-service ingestion, transformation, and exposure of data products for IT and business teams.
• Lead engineering and run activities across distributed teams in France, LATAM, Italy, Czech Republic, and wider EMEA, acting as a technical coordination point

• Engineered large-scale Azure data platform solutions using Azure Databricks, Azure Data Factory, and warehouse-oriented analytics patterns, supporting enterprise datasets across Spain and Portugal in multi-terabyte to petabyte-scale environments.
• Improved Power BI performance for priority workloads, reducing report load times by approximately 65% through query optimisation, data modelling, and

• Supported enterprise customers on advanced data platform, migration, and performance issues while moving from traditional Hadoop / Informatica-style estates to cloud-native Azure architectures.
• Built, tuned, and troubleshot ETL/ELT pipelines using Databricks, Hive, and Synapse across production and pre-production environments.
• Stabilised data warehouse workloads through root-cause analysis,

• Built data pipelines supporting both real-time and batch processing for IoT and mobility analytics within Bosch Mobility ecosystem.
• Contributed to connected vehicle platforms processing telemetry data for fleet and mobility analytics, supporting use cases such as driving behavior analysis, damage detection, and operational monitoring.
• Supported integration of vehicle data into APIs and dashb

• Executed a phased migration of a data warehouse from on-premises to AWS using S3, Redshift, and Glue, achieving approximately 40% faster processing and ~30% lower infrastructure cost.
• Maintained data integrity and business continuity throughout migration with no business downtime during transition.
• Rebuilt pipelines with Glue, Lambda, and Spark, introducing stronger partitioning and incremen

• Refactored data pipelines using Apache Beam and Airflow to improve maintainability and orchestration reliability.
• Tuned warehouse structures and delivery patterns for BI consumption and analytics use cases.
• Applied BigQuery partitioning and clustering to improve query performance and cost efficiency.
• Used Cloud Composer to automate recurring workflows supporting enterprise dashboards and d

• Built AWS Glue-based pipelines for telecom analytics and contributed to warehouse-side performance tuning in Amazon Redshift.
• Optimised query and reporting structures, including materialised-view-based patterns for analytics consumption.
• Supported reporting integration through Tableau and Qlik.
• Applied governance controls with IAM and KMS for secure data access.
• Delivered datasets suppor

• Built data solutions for financial and credit-related workloads using Azure Data Factory and Databricks.
• Applied Delta Lake patterns for batch and near-real-time data processing.
• Optimised Azure Data Lake usage with improved partitioning and access patterns.
• Implemented governance-aligned controls with Key Vault, RBAC, and auditing.
• Supported delivery of risk scoring KPIs and reporting d

• Contributed to telemetry-oriented data solutions in a connected mobility context spanning 23 countries and supporting environments with 2.6M+ active vehicles.
• Worked on Azure-based data flows using Event Hubs, Stream Analytics, and Data Lake for telemetry ingestion and downstream analytics.
• Used Azure Data Factory to prepare datasets for analytics and ML-oriented use cases such as fleet moni