Marwane Goudani

Contributed to the modernization and structuring of the company’s Data & BI ecosystem within a fast-growing leasing and financial services environment. Held a highly cross-functional role combining analytics engineering, data analysis, BI delivery and GenAI PoCs, working closely with Finance, Risk, Marketing and Executive stakeholders. Key Responsibilities & Contributions : • Contributed to the evolution of a modern data platform built on Snowflake • Developed and maintained scalable ELT pipelines using dbt (modular modeling, testing, documentation, environment separation) • Built ingestion and transformation pipelines from multiple source systems (ERP, CRM, operational databases) using advanced SQL • Designed and implemented custom Fivetran connectors, enabling ingestion from external APIs (e.g., OpenDataGov) and internal systems • Architected data landing and processing layers using AWS S3 and AWS Glue • Set up and configured AWS MWAA (Managed Airflow), developing and orchestrating DAGs for end-to-end workflow automation • Contributed to infrastructure management using Terraform and GitLab-based CI/CD workflows • Designed analytical data marts and business-ready datasets for Finance, Risk and Operations • Developed and optimized executive dashboards in Power BI (data modeling, DAX, performance tuning) • Implemented automated governance and monitoring tools for Power BI reports (usage tracking, access control visibility, reporting oversight) • Collaborated directly with business stakeholders to translate operational needs into scalable analytical solutions Technical Stack : Snowflake · dbt · SQL · Python · GitLab · Fivetran · AWS (S3, Glue, MWAA) · Terraform · Power BI · Oracle · MariaDB · SQL Server

- Oversaw the processing and governance of large volumes of financial and transactional data. - Coordinated cross-functional teams to implement AI models aimed at detecting fraudulent activities. - Managed the automation of tax compliance audits, significantly reducing manual workload and error rates - Ensured timely delivery of data solutions, aligning technical implementation with business goals and regulatory requirements.

- Design of solutions to industrialize the use of data in operational processes, with a focus on data collection, storage, and transformation. - Analysis of various databases to improve customer experience by identifying trends - Feasibility study of an AI project: Churn prediction. Main tools: Python (Pandas, Pyspark, Sci-kit learn, Plotly,..),SAS, SQL

- Developing a AI model for Parkinson's Disease early alerting - Researching and implementing custom statistical models and Machine Learning algorithms for classification and regression - Collaborating with Ph.D. Researchers in order to explore complex Machine Learning algorithms - Hands-on Deep Learning frameworks (TensorFlow, Keras, PyTorch, Caffe) - Testing with pre-trained Deep Learning models (ResNet, AlexNet,..) - Communicating results and ideas to the team - Sharing code and resolving issues through GitHub Tools: Python (Jupyter, Pycharm), SQL, Git, Docker, Google Colab.

- Managing a team of five software engineering students - Activity and resource planning - Analyzing and managing project risks - Collecting data from various factories - Controlling time management - Cost estimating and developing the budget - Working on a Virtual Reality project for Industry 4.0 - Creating a 3D model of a real-life factory using Blender - Creating 3D models of workers and simulation of their work - Development of a Windows-based application using .NET framework - Communicating results to the customer and ensuring his satisfaction - Making multiple reports and presentations showing the progress of the project Tools: Python, GranttProject, Blender, Visual Studio, MS Powerpoint, MS Excel.

- Extracting and enhancing data used in the training of an intelligent job matching algorithm - Data wrangling, cleansing, and exploration - Identifying and manipulating structured and unstructured data sets from various sources - Implementing innovative quantitative analysis - Experimenting with new visualization tools - Developing a solution to optimize the Machine learning model - Communicating with internal and external clients to understand business needs and provide analytical solutions Tools: Python, Pandas, sci-kit, SQL, Tableau, MS Excel

- Statistical analysis of turbines - Data Collection and extraction - Cleaning and Preparing data for analysis using Python - Extracting useful insights from collected data - Graphical representation of data using Excel Tools: Python, MS Excel