Deschanel M.

Project context: Implementation of a data platform to centralize data from business applications and exploit it to aid in the monitoring and tracking of projects within the group

Team: 5 FTE, Kanban, Jira

Objectives: Improve the monitoring, efficiency, and communication with partners of its activity to optimize its organization, secure financial flows with its partners (Stellantis, Renault, etc.), and equip itself with tools adapted to the regulatory context.

- Participation in functional workshops to gather business needs

- Participation in technical workshops to analyze different business systems (SAFIR, etc.)

- Participation in the implementation of the technical architecture (IPaaS & Data).

- Implementation of processing flows feeding the Data Lake using ETL Talend Studio

- Participation in writing functional and technical specifications, Data Dictionary

- Data Warehouse modeling and data loading flow design

- Extraction of raw data from business applications.

- Development of platform data loading flows (IPAAS + DATA) using ETL (Talend Studio, ADF, and Azure Databricks)

- Technical Integration Testing (RIT)

- Deployment via Azure DevOps CI/CD and delivery to the client.

- Writing operational and production documentation

- Change management (supporting the client in adopting the new Power BI tool)

- Transfer of skills to internal teams.

- Correction of anomalies encountered by the client

Tools/Environment: PostgreSQL, SQL Server, Oracle, ETL Talend Studio 7.3, Azure Data Factory, Azure Databricks, GitLab, Talend Cloud, Azure DevOps, Power BI, Citrix, Redmine, PuTTy, PySpark, SparkSQL, Shell

MISSION:

Within DIOR's ETL service center, composed of a central team of 16 people responsible for the entire BI integration platform of the group, and in close collaboration with the Component Teams (product teams), my mission is to respond to the needs and support DIOR's product teams (Shoes, Dior Manufactures, Ready-to-Wear, Customer Data, HR, etc.) by providing them with expertise in their various defined challenges.

TEAM: 16 FTE, Jira, Miro

PROJECTS: Diorchestra, ByDior, LVMHappening, DiorCare, CIM (Consumer Infos Management)

- Participation in functional workshops to gather needs.

- Participation in technical workshops to identify and analyze different business systems (Salesforce, COR, UCR, CEGID, Shopify Me, CRM, Dataiku, LVMH Global Data, CDIL, SAS, Cristal, Enterprise Data Platform, Local China Customer Data Platform...)

- Review of retro-documentation of technical and functional specifications, detailed design document.

- Decommissioning of Datastage flows for Talend flow implementation.

- Migration and optimization of existing Talend flows.

- Development of new ETL flows on TALEND using APIs.

- Unit and integration tests.

- Publication and promotion of Artifacts on TMC (Talend Management Console)

- Creation and configuration of tasks on TMC.

- Preparation and deployment of the OpCon delivery package

- Scheduling of Opcon Schedules.

- Automated running of scheduled tasks on different environments.

- Handling of testing anomalies.

- Preparation of CHANGE and participation in the CAB (Change Advisory Board).

- Go-live and communication to users.

- Provide technical support (Hypercare post Delivery).

Tools/Environment: ETL Talend Cloud API Services Platform, AS400, TMC (Talend Management Console), OpCon, KeePass, FileZilla, SQL Server, DBeaver, SSMS, SQL, GitLab, Kanban, Jira, SAS, Salesforce.

Project context: Project for collecting and processing Financial data to meet the regulatory needs of the retail bank

Team: Wekan (Kanban), Jira

Objectives: Within a 'business' team of 12 people, in close collaboration with the 'BigData Expertise Center of Société Générale', I have a mission of Data analysis and expertise on issues defined by the business within the framework of regulatory projects concerning financial data, third-party data, and data quality of commitments, in an Agile project context.

- Gather business analysis needs and propose their translation into algorithms

- Model and load data into a DataLab (HDFS)

- Schedule data ingestion and integration (Third-party, Service, Collateral) from data warehouses via Sqoop to the DataLake (Hadoop) using Oozie

- Implement functional controls on data to detect anomalies (Hive, T-SQL)

- Monitor and improve the performance level of data processing according to needs and their integrity

- Implement ergonomic solutions for presenting processing results according to business needs

- Enrich existing knowledge documents on the data concerned

Tools/Environment: Hadoop Distributed File System (HDFS), Hive, T-SQL, Shell, Oozie, Sqoop, Hue, Teradata, Github, Citrix, Kanban(Wekan), Impala