Ahmed Amine BEN SALEM

.Lead end-to-end Data & BI projects across Finance, Customer, Salesforce, Ticketing, and Operations domains, from requirements to production delivery. .Provide technical leadership and define development standards, architecture guidelines, best practices, and code quality principles across Data & BI projects. .Design, develop, and optimize scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Azure Databricks, Spark, Python/PySpark, and SQL. .Oversee the Azure Databricks platform, including cluster configuration, policies, performance optimization, monitoring, access management, and cost optimization. .Drive platform governance and security, including access reviews, service principals, Key Vault integration, linked services, certificates, and environment configuration. .Lead Azure DevOps and CI/CD practices, including branching strategy, version control, deployment automation, environment synchronization, and pipeline troubleshooting. .Coordinate platform upgrades and technical migrations across Databricks workflows, libraries, clusters, policies, functions, and CI/CD components. .Ensure platform reliability through proactive monitoring, incident management, root cause analysis, and coordination with Microsoft and Databricks support teams. .Design and maintain Azure Analysis Services semantic models and deliver Power BI dashboards and reports with advanced DAX and optimized data models. .Drive performance optimization across Databricks, Spark, SQL, data pipelines, and BI workloads. .Coordinate production deployments, release activities, hypercare, technical actions, and resolution of critical blockers. .Drive continuous improvement, platform housekeeping, technical debt reduction, documentation, and operational best practices. .Mentor and support team members, facilitate technical onboarding, and provide guidance on development and platform standards. .Act as a technical interface between business stakeholders, Data/BI teams, infrastructure teams, and external technology partners. .Technical Environment / Tools: Azure Stack, Azure Data Factory, Azure Databricks, Azure Analysis Services, Power BI, DAX, SQL, Python, PySpark, Azure DevOps, CI/CD, Git, Salesforce, ETL/ELT, Data Modeling, Tabular Cubes, BI Reporting, Windows, Linux.

End-to-end data and BI project management on Azure and Databricks, from requirements gathering to final delivery of BI reports: .Project Coordination and Management: Leading the team, developing technical specifications, creating planning dashboards, prioritizing tasks, tracking and monitoring the project, and organizing workshops and co-projects. .Data Ingestion Pipeline Development: Implementing data ingestion flows on Azure Synapse from various ERP sources (M3, JDE, SAP, K2, etc.). .Medallion Architecture Implementation: Technico-functional management of data transformation in Databricks (Bronze, Silver, Gold). .Best Practices for Code Management: Creating packages and .whl files on Databricks to promote reusability and deployment. .Industrialization and Orchestration: Implementing continuous integration processes for BI solution and machine learning model deployment, as well as orchestrating data flows via ControlM or Airflow. .Continuous Improvement: Production monitoring, incident management, and operational maintenance of reporting. .Agile Management with DevOps: Using Azure DevOps for sprint planning, backlog management, and task tracking, facilitating team collaboration. .Versioning and Deployment: Managing version control with Git and Azure DevOps, automated deployment of solutions, promoting reusability and efficiency. Technical Environment / Tools: Azure Synapse, Databricks, Unity Catalog, Delta Lake, Azure DevOps, Python, PySpark, Scala, SQL, Git, VS Code, SQL Server, DBeaver, Teams, SharePoint.

Support for the migration to a Big Data infrastructure on Azure, optimization of the data model: . Project Leadership by collaboration with security, IT, business, and development teams . Data Processing and Pipeline Implementation by using Azure Databricks, including the implementation of Azure Data Factory pipelines . Optimization and Version Control through Azure DevOps, Enhance performance of Databricks Notebooks, optimizing Python code for better efficiency . Machine Learning Development: Create and monitor machine learning algorithms for traffic prediction, utilizing MLFlow for performance optimization and monitoring. Technical Environment / Tools: Python, Azure, Databricks, Azure Data Factory, Azure DevOps, MLflow, PySpark, Scala, Spark, SQL, .csv, .json, .pkl, scikit-learn, Unix, Windows.

. Process Optimization by implementing best practices including standardization, versioning, automated linting, and code factorization. . SAS Python Migration Project to ensure effective transition. . Python Support for data scientists in improving their Python skills. . Best Practices Charter by Establishing a charter of good practices for Python development

Data analysis and exploration using data visualization and data science tools. .Collaboration and Coordination: Working with business teams and developers to understand requirements and coordinate the project. .Audit and Analysis Strategy: Auditing data quality and designing the analysis strategy, exploring IoT data. .Data Preparation and Supervision: Preparing data in Python for PostgreSQL, supervising and correcting data quality. .Machine Learning Projects: Predictive analysis of oil degradation, using classification and segmentation algorithms. .Data Visualization: Designing dynamic data visualization tools with Bokeh in Python. .Code Industrialization: Industrializing code for production deployment, ensuring maintainability and design integrity. .Performance Optimization: Improving performance (execution speed, file reading, database loading via Bulk methods). .Versioning and Automation: Managing version control with Azure DevOps, using Docker to automate scripts. Technical Environment / Tools: Azure DevOps, PostgreSQL, Git, Docker, Python (Pandas, NumPy, Scikit-Learn, SQLAlchemy, Seaborn, Bokeh), Jupyter, PyCharm, JSON, Linux, Windows, Dataiku.

Leading Data Science projects Analytical solutions for the business - marketing team and innovation • Sensor data exploration (IOT) and data analysis. • Time series Clustering of energy consumption curves, • Customer segmentation, • Predictive consumption analysis • Presence detection

Definition and validation of risk calculation methodologies, along with the control and monitoring of data quality. Data governance and quality Statistical analysis Simulation and forecasting Market risk calculation methodologies Data management

Conducting research on performance and risk measurement - Micro-économie comportementale. Forte composante technique (mathématique et informatique).

Pricing of derivatives and exotic products on equities and indices. Maitrise des instruments financiers et les outils informatiques (R, VBA)