Paul -

Currently supporting cross-functional business teams and decision-makers in analyzing multi-channel sales data and web traffic within a challenging legacy migration context to GCP. Core Competencies: Data Transformation: Building robust dbt models following Richemont best practices for scalable data pipelines Orchestration: Managing complex workflows with Apache Airflow for reliable data processing Visualization: Creating actionable insights through Looker dashboards for business stakeholders Platform Engineering: Supporting critical GCP migration from non-terraformed legacy systems Technical Leadership: Leading development teams and coordinating 2 developers on data platform initiatives Cross-functional Collaboration: Direct engagement with business teams and decision-makers across all company functions Current Focus: Supporting enterprise-wide data analytics initiatives during a major legacy system migration to Google Cloud Platform. Working closely with business stakeholders to analyze multi-channel sales performance and web traffic patterns, while ensuring data quality and accessibility throughout the transformation process.

At Moët Hennessy, I held the position of Platform Engineer within the CoE, where I helped optimize the data platform to facilitate application development and deployment. My role was to design tools and workflows enabling development teams to benefit from off-the-shelf code, while guaranteeing the security and conformity of their deliveries. ACHIEVEMENTS Development of data products to monitor the platform, including analysis of 700 GCP projects, analysis of GitLab repositories and implementation of Health check of various services (Kubernetes, Composer, DBT, BI Portal). Implementation of verification and monitoring processes to ensure the availability and performance of deployed services, thus contributing to the optimization of operations. Creation of reusable Python packages, Composer operators, ci templates and DBT macros, facilitating development and integration for development teams. Proactive maintenance of the platform, including regular updates of various packages and airflow to guarantee service performance and security. Recommendations on design patterns around data, with a focus on security, architecture and financial operations (FinOps).

S3ns is an alliance between Thales and Google, whose aim is to provide a "trusted cloud" offering that meets the criteria of ANSSI's SecNumCloud label, based on public GCP source code. Within the Analytics team, the aim is to be able to operate GCP Services in production in order to maintain our SLOs, as well as to build an on prem analysis stack on Kubernetes clusters. As such, we are fortunate to benefit from Google's internal training courses on SRE best practices.

⬥ GALEC Data Engineer GCP August 2021 - November 2023 Implementation of a platform for collecting various historical data flows. ACHIEVEMENTS - Development within a squad (3 squads on this project) of 28 devs and 17 Po of a dockerized application managing file ingestion (avro, csv, json, ascii, xml) and api response to Bigquery. - Management of raw, bronze and silver models on Bigquery via dbt - Infrastructure management via terraform - Unit testing with dbt and Python - Orchestration via Airflow - Process management and monitoring (error alerts) via dbt TECHNICAL ENVIRONMENT Google Cloud Platform, Airflow (Cloud Composer), BigQuery, DBT, Python, Docker, Pub/Sub, GCS, SQL, Shell scripting, Terraform, Azure DevOps ⬥ GROUPE LES ECHOS - LE PARISIEN Data Engineer GCP March 2021 - July 2021 ACHIEVEMENTS - Evolution and creation of DAGs ( Batchs ) for raw data retrieval and ETL pipelines. - Implementation of new dashboarding using BigQuery to support ingestion tracking - Cloud function scheduler, deployed via Terraform to ingest data from an API (Batch). TECHNICAL ENVIRONMENT Google Cloud Platform, Airflow (Google Cloud Composer), BigQuery, Python, SQL, Shell scripting

Implementation of fraud detection tools using static rules or ML algorithms. ACHIEVEMENTS ⬥Data analysis and segmentation of physical customers by implementing algorithms performing searches on graph databases. ⬥Purge of a storage server containing identity documents. Requiring image processing, facial and character recognition. Program in Python and Tensorflow, packaged in a customizable Docker Image. Put into production via Jenkins / Kubernetes. Project carried out alone (except for the Kubernetes part). ⬥AutoML POC performing automatic in-store fraud labeling. ⬥Data analysis and segmentation of physical customers by implementing algorithms performing searches on graph databases. ⬥Wrote multiple SQL queries to label different cases of fraud. ⬥GCP project administration TECHNICAL ENVIRONMENT Python, Google Cloud Platform, BigQuery, Cloud Storage, Data studio, Cloud Function, App Script, Docker, Tensorflow, AutoML

BassMe+ is a next-generation personal subwoofer that allows you to physically feel the sound differently than just listening. Placed on your chest, BassMe+ propels you to the heart of the sound, thanks to its system of sound waves and vibrations.