Gaspard J.

Michelin is an international leader in tire manufacturing. The DORD department aims to innovate in mobility by making it "always safer, more accessible, more efficient, and more environmentally friendly." These four levers drive our innovation in both products and services.

Summary: modeling and application development, Big Data / IoT context

Data Exploitation:

- Creation of tables, views, and collections, data quality improvement and enrichment

- Development of associated jobs

- Data mining

Orchestration:

- Improvements to routine processes (execution times and quality)

- Integration of (n-)structured data

Data Manipulation & Transformation:

- System audits, evaluation of current systems, vectorization of possibilities,

- Parallelization and distribution of operations

Data Science:

- Support in implementing responses and solutions for various users (majority of Data Scientists)

Data Valorization:

- Data Visualization, creation of reports, indicators, and applications facilitating exploration

- Competitive context with an interest in solutions that promote results

- Information gathering through layers and KPIs

Technical Environment:

Technologies: Python (pandas, scikit-learn, csv, json, re…), SQL, noSQL, Pyspark (Spark)

Tools: Gitlab, Dataiku, Airflow, Anaconda, Artifactory

Data Viz: PowerBI, Plotly, Pydeck, Bokeh, Kepler, Tableau

Azure Ecosystem: Datalake, Databricks, DataFactory, Azure DevOps

Oracle Sql Developer

Azure Cloud, Azure Data Lake

Skills: Data Engineering · Data Science

I assist my clients from A to Z with their data exposure needs:

• Solution design and modeling,

• Data collection & exploration (API, DB, Web Scraping, etc.),

• Associated developments (python, ETL / ELT, job, pipeline, orchestration, CI/CD etc.),

• Storage and database modeling (DWH, Datalake, SQL, relational DB, NoSQL, document-oriented, etc.),

• BI & Visualizations (reports, datamarts, KPIs),

• Data Science (Machine Learning, Big Data, Cloud and process automation),

• Support & training.

As a Data Engineer (Data Scientist and Python/Pyspark developer), a Capgemini contractor, my objectives were to expose data to generate associated KPIs and reports.

Mission: data exposure and valorization.

Reporting to the S2P department, the project aims to make various data sources available and meet business requirements:

• Expose data stored in the Datalake using the Delta Lake architecture in Azure Databricks,

• Creation of data layers (using Pyspark) according to the BSG (Bronze, Silver, and Gold) model,

• Data processing in "Batch" and real-time (streaming) modes,

• Continuous integration and delivery (CI/CD) with Azure DevOps,

• End-to-End exposure,

• Training and upskilling of junior profiles.

International team

Technical Environment:

Python (pandas, Scikit-learn, CSV, JSON, RE…), Pyspark (Spark), SQL

Databricks, Data Factory, Azure DevOps, Oracle SQLDeveloper, Power BI

Azure Cloud, Azure Data Lake