Emmanuel Hootelé

Priority to Data, I continued to make the link between its aspects in order to create all parts of modern data-platforms, in the broadest sense, using adapted technologies and concepts as: Analysis, Architecture, Engineering, Business Intelligence (BI): ETL, databases systems (SQL, NoSQL), Automation (CI/CD & DevOps), Data Ops, Cloud, etc. Examples of customer projects: • Government | Belgium (2025): Expertise (Tech Lead) via specialized team on architecture and technical issues, leading to the complete development of a Cloud-oriented data platform. Audit of existing systems, standardization, documentation, concepts and implementation of high-performance solutions, coaching of teams. Examples of technologies: NoSQL, SQL Server, PostgreSQL, Event Hub / Kafka, Azure, Data-Factory, Databricks, Python, ETL, Delta tables, Data Lake, Lakehouse, Data Ops / DevOps, Governance, ... • Bank & Financial | Belgium (2024): PoC architecture & development to migrate & synchronize a DataMart / Datawarehouse on Cloud (Azure), using DevOps, Power Shell, etc. • “NATO" Organization | NL - The Hague (2024): Specific project to fully and correctly take over an "MvP" for ingesting data from APIs and various ERPs into a unified Data Lake (SQL, Data-Factory, Spark / PySpark, Databricks, DevOps, …). Summary: - Audit existing tools, advice on Data & Cloud Azure architecture. Coaching and documentation for off-shore developers, so that they can start again in 2 weeks what had taken 2 months to get right. - Development of solutions and improvement of technical processes (standardization, performance). • CRM solutions editor (IT)| France – Paris (2023-2024): For its local and international use, a specific project concerning several axes related to “Data & Cloud”. Based on a functional analysis, obtaining sponsor’s prerequisites and creation of technical solutions. Main details worth mentioning: - Audit of data usage to verify its accuracy and fitness for extended use on a new platform, including “BI/Big Data” and even “AI/ML”. Greenfield migration to Azure under concepts of Data-platform, Data Warehouse, Lake house. - Architecture of infrastructure components and ingestion to the Cloud (SQL-Server, SSMS, Azure, DevOps, IaC) - Complete data engineering processes, from data synchronization, preparation, and transformation to optimized storage (SQL, ETL, Python, Databricks, CI\CD), governance and security. - Business Intelligence based on the processed data, creating different reports according to the uses of the company (Management, Team Leaders, Members), in conjunction with the above-mentioned tech solutions. (Power BI, DAX) • Government | Belgium - Bruxelles (2023): Practically, “Solution Architecture” to improve usage of “Data” products, combining Engineering and Science (AI/ML). • Energy sector *| Belgium & Germany (<=2022): - Architecture, creation, documentation, and use of data-platform (Data-lake, Delta Tables, Azure) and of key components (MVP) usable by the whole company, including Cloud services and data. - In a multidisciplinary R&D-oriented team, design an innovative project dedicated to automating manual processes. Complete “PoC” from architecture to implementation using Web, Data, Cloud, AI/ML, etc. - Refactorization and improvements of existing SSIS, Data-Factory, DevOps pipelines for the sake of increase of governance, performance, functionalities, and user’s thrust. Reducing the number of items and codes to manage. - Development of a “Datawarehouse & Lakehouse” system to generate specific ready-to-use reports for end users. - Data & DevOps pipelines and automation processes to create migrations (SQL, Databricks / Synapse). - Application of Agile principles, technical project management localized with some tradesmen of the business. - Project improvements during the covid crisis, then creation of Big Data solutions (Python, Spark, Hive, SQL) to ingest data from local systems (API, SQL-Server, Files). Discussions with field technicians and business users to get proper requirements and to propose an efficient standard solution to reduce the ingestion time, infrastructure costs, allowing batch and real-time lambda mode.

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