Awa T.

• Study of the existing system,

• Gather new business needs and define new KPIs,

• Implementation of a Core Reporting based on a Finance purchasing taxonomy solution, using Power BI, for the Central Finance Department, as well as the group's main business lines.

• Implementation of security rules at column and row levels

• Optimization of the existing data model

• Facilitate business workshops and gather needs (Finance)

• Scope the solution with a pragmatic approach,

• Write user documentation for business users,

• Development and optimization of SQL queries in BDT

• Data transformation with DBT

• Dataset versioning and deployment pipeline implementation

• DAX development and optimization of created measures.

Context

In a context where data collection, management, and analysis are crucial, the Malthouse BI project was developed to strengthen decision-making processes within the Invivo Group. It specifically focuses on the group's Malt House division, offering the ability to monitor stock levels, purchases, sales, and the operational position of various entities.

Missions

• Study of the existing system, Gather new business needs and define new KPIs,

• Development of pipelines with Azure Databricks DLT.

• Development in Python (PySpark).

• Creation of optimized data models.

• Optimization of SQL queries.

• Implementation and maintenance of an optimized dataset.

• Modeling of a data model with Power BI.

• Consolidation of data from SAP and JDE.

• Monitoring compliance with Power BI development best practices and report development.

• Supervision and correction of anomalies in reports.

• Optimization of data models.

• Migration of ETL processes from Azure Synapse to Databricks.

• Integration and cleaning of multi-source data.

Technical and functional environment

Power BI, Databricks dlt, Azure Data Factory, DAX, M Language

The CCOE is a unit of the L'Oréal group that manages, deploys, and maintains APIs within the group. The objective of this project was to implement a data visualization solution to track API usage via APIGEE (Google's API management). We were a team of 9 people working in an agile manner.

Missions

• Gathering needs from business units, Gather new business needs and define new KPIs,

• Definition of KPIs,

• Modeling of optimized Core Models and implementation of datasets with Power BI,

• Report development and maintenance,

• Development of Python functions for data integration,

• Development of SQL queries with BigQuery,

• Design of the data model, reports, dashboards, and deployment of reports,

• Identification and resolution of performance issues in reports,

• Training internal users on the use of implemented reports,

• Technological watch on Power BI for the evolution of existing reports,

• Maintenance of existing reports and model optimization,

• Development of Power Automate triggers,

• Testing and acceptance to ensure data quality.

Technical and functional environment

Power BI, Azure functions, DAX, M language, GCP, Python, Bigquery, Azure Storage, Apigee, Web service restPostman, SQL, Agile,

Retail, SAP.