Robin Fromentin

I worked on data flows and made objects available in the Snowflake database using an ETL tool called Semarchy xDI.

I also worked on setting up the SQLMesh tool to create data pipelines on a medallion architecture.

I am involved in the entire Data Snowflake scope of the company.

Snowflake is used as a Data Warehouse and centralizes company data.

I joined to continue its implementation.

My tasks were to retrieve the company's operational data in real-time by developing Talend ESB and batch flows to Azure Blob Storage.

Then develop real-time data pipelines via Snowpipe, and consolidate the data into final tables. All this to make it available for Dataviz tools, or for training AI models.

I was responsible for all the administration and data reference aspects available in Snowflake.

First, I joined Chronodrive to implement a project for populating a MySQL Data Warehouse with data from operational sources. The goal was to build a repository of consolidated and cross-referenced data from different sources to meet business data analysis needs. My mission also consisted of optimizing existing data flows, evolving them (TMA), and managing the daily RUN.

My mission then evolved following Chronodrive's desire to move to a more powerful Data Warehouse. I participated in POCS and comparisons of the solutions considered: Snowflake was chosen. I took part in workshops to define the new architecture of this DATA platform with all decision-makers and stakeholders.

These workshops led to the creation of the entire Snowflake database architecture, as well as the creation of an Azure cloud repository that would allow data to transit to Snowflake. I was responsible for this configuration and the creation of this environment.

I developed generic flows to retrieve data from operational environments to populate Snowflake, with the aim of managing only configuration and not as many data flows as entities to retrieve (tables, files, etc.).

-> generic flows: extraction of data from operational sources to Azure Blob Storage.

-> generic flow: import data from files into Snowflake.

-> generic flow: transformation and cleaning of data between two Snowflake schemas.

-> data flow per consolidated table, which cross-referenced them, aggregated them, and performed calculations (for Dataviz & Data Science)