Cédric Hidot

As Lead Data Expert at Pernod Ricard Data France, I ensure that all business requirements are translated into technically sound, sustainable, and high-performing data solutions. I oversee the design and reliability of our data ecosystem built on Snowflake, dbt, Matillion, and Qlik Replicate/Compose, structured with a medallion architecture and aligned with data mesh principles. Each day, I monitor ETL health and execution status, resolving issues across Qlik, Matillion, and Snowflake to maintain data freshness and reliability. I’ve been officially assigned to oversee critical financial data refreshes up to six times daily, including weekends and holidays, particularly during year-end closings. These are orchestrated via Automic, which triggers Compose loads, Snowflake transformations through Matillion, and QMC reloads on Qlik Sense BI. I led the full recovery of the Vista KDP platform, a strategic project analyzing promotion performance using internal ERP data (JDE) and external sources (Nielsen, Intermarché, A3Distrib). I rebuilt it end-to-end pipeline—Qlik to Snowflake to API—and now lead its migration to a dbt-native stack, improving modularity and governance. I also develop advanced tools such as the Stock Secu algorithm, written in Python, which calculates optimal inventory by analyzing sales, forecasts, stock, and supply constraints—balancing service levels and capital efficiency. I'm also in charge of validating the PR of the Data Engineering team. As the technical reference for architecture, data modeling, and performance optimization, I support complex projects from design to delivery, coordinate with global data teams, and drive the evolution of our platform. I'm the go-to expert for resolving critical incidents, enabling cross-domain integration, and ensuring every data product meets both business and technical standards.

As Data Expert in charge of Architecture and Data Analytics at Pernod Ricard France: Maintaining evolutions of Snowflake data model across Bronze, Silver, and Gold layers. Key responsibilities and achievements: Data Architecture and Strategy: • Applying Data Vault concepts and Data Mesh principles for scalable modeling • Enhanced data discoverability, reusability, and governance • Collaborated with Product Owners to translate business requirements into technical specifications • Managed development using Jira and Github across Dev, UAT, and Production environments • Implemented data quality processes and documents for BI teams System Modernization: • Migrated legacy security stock calculation from Informatica to Azure Data Factory and Databricks • Integrated legacy processes into Snowflake model • Refactored JavaScript and SQL stored procedures for migrations • Retro-Engineered a databricks algorithm for safety stock calculation (Python) Data Monitoring Dashboard Achievement: Developed a monitoring app from POC to production for real-time data ecosystem insights Features: • Unified monitoring for multiple systems (ETL, Data Warehouse, Visualization tools) • Real-time status updates for key tools (Azure Data Factory, Snowflake, DBT, Qlik Compose, Power BI, Qlik Sense) • Automated reports and interactive visualizations • Custom KPIs and success rate calculations Technologies: Python (Streamlit, Pandas, Bokeh), Azure services (Data Factory, Log Analytics), SQL, Snowflake, HTML/CSS, Git Skills Developed: • Full-stack development of data-intensive applications • Cloud integration and pipeline optimization • BI tool integration • Agile project management and data visualization Impacts: Reduced monitoring costs while providing tailored insights, demonstrating ability to deliver enterprise solutions that enhance operations and decision-making These initiatives improved operational efficiency, data reliability, and business agility at Pernod Ricard France

As the Data Product Owner and Data Analyst I’m responsible of : Defining a proper backlog and introduce a product driven approach for the team. To do so, I’m implementing a Craft.io backlog and roadmap that will be connected to the team Jira space. I will introduce sprint, sprint review to implement an agile approach. Snowflake and Tableau Dashboards will be considered as Data products and therefore will have correlated roadmaps with dependencies. This is visualy maitained on Craft.io. This product owner is a part time job, during the other part I’m Data Analyst, consolidating data on Snowflake by creating views. We have 3 environnements: Raw, Stage, BI_Warehouse. Working closely with the COO of the company to deliver a Procurement Dashboard that will track the procurement costs and volumes to make efficiencies. I’m extensively using Chat GPT 4 to do some SQL statements and python scripts on Snowflake. Tool used: • Snowflake • Tableau Desktop • Craft.io • Jira • ChatGPT

As a Senior Data Manager, I’m responsible to assess all the tools of the company, in order to draw the data models, data dictionnaries and business process models. We are talking here about 15 tools, from Salesforce to several Project Management Tools (LBS, XTRF, Escaepe, FX Tracker …) and SAP. This work is the basis to better understand the data and the patern to consolidante everything on Snowflake. I’ve designed the documentation of Snowflake which is the Core Data Model of the company. On this Core Data Model, I’m describing standard objects that are used by the company and all the correspondances on the source systems. Leading the Data Taxonomy initiative. I’ve implemented standard list for strategic data object (Customer, Language, Country, Business Unit, …) by doing so, we could do mapping from Taxonomy to source systems to better consolidate and streamline the data. I’m also Database Administrator of the « db Taxo » where i’m implementing and maintaining all the mappings for the company. This is usefull for Snowflake consolidation and for tool migrations. For this initiative, I’ve introduced a business validation, with business owners that have an expertise in a specific Domain and have the responsibility to validate any new request. To make it efficient, there is a Jira Space for any addition or change request on the Taxonomy. I’m also managing a documentation on Confluence, keeping up to date each month the Taxonomy list with Confluence blog post and emailing communication. Leader of the data team on the Customer Data Model initiative to align the definition of how do we consider a customer (which are the data fields that are mandatory ?). Aligning Salesforce, One PM tool and SAP. RACI with key stakeholders and Data Architecture & Flow diagrams to track the path of the data. Tool used: • Data Grip => SQL DB : Oracle, MySQL, Postgres, SQL Server • Confluence • Tableau Desktop • Tableau Data Management • Draw.io • Postman • Jira • Salesforce • SAP

As Data Manager, my role is primarily to implement with the support of the tech team, a new data catalog solution within Air Liquide. After an elaborate benchmarking, we have selected Azure Purview. Azure Purview was a natural choice for the organization as we have a lot of experience with Azure solutions like: - Power BI - Azure Data Factory - Azure Synapse & SQL - Azure Storage We are developing for our own need and usage some assets that are not natively supported by Purview as Analysis Services assets and AWS environment (S3 - EMR - Table - Athena) Today, our data glossary assets are registered on Gdoc and Gslide ressources, which is siloted and not really efficient. Tomorrow with Purview, we will be able to integrate Data Glossary terms with a complete description, define an ownership and link data asset to these terms. Also we will link the Data Model ressources directly on these newly created Glossary terms. To conclude, Purview offer a lineage to understand from the source to the target, all the interconnection between our data assets. In order to properly integrate and deliver this solution, I have to manage the ressources (humans and budget), the timeline (planification with Gantt) and the backlog with the prioritization of the features we want to implement. https://www.youtube.com/watch?v=ZO5fcsN4tjc In a near future, my responsibility will be extended to implement a Data Portal, combining the Data Catalog solution based on Purview with Data Hub developed by AWS and the Jupiter Lab connected Notebook. This solution will offer a single layer for all type of users (Data Officers, Data Analyst, Data Scientist, Data Domain Managers). Another brick of the Data Manager responsibility in Air Liquide, is dedicated to help the data governance strengthen and streamline the alignment between Core Data Models and Data Platforms architecture (Azure / AWS Data platforms). I’m finally a support and adviser to the business for any Data Model to be constructed.

Analyst in the Industrial Data Analytics team in the Large Industries Worldwide Industrial Management (LI WIM) entity, my responsibilities are spread over the entire data processing chain, from modeling to Business Intelligence, including data acquisition and processing. As an analyst, my activities cover: • The design and deployment of business intelligence pipelines via analytical processing, data mining (SQL query, SQL Views, SQL Stored Procedures, Functions ...) and data visualization tools (Power BI) for performance monitoring • Administration of MDS / ODS / DWH SQL database + SSAS Tabular Cubes • Carrying out exploratory analyzes in support of the industrial strategy As manager and administrator of the industrial reporting tool LIRE (world scope with more than 520 production sites, 1200 users) I am in charge of: • Modeling business needs for the acquisition of relevant data (Integration of new KPI and new analytical tools to reach the highest standards of operational excellence) • Ensure the quality of the data to maintain performance indicators data quality regression • Continuous improvement of reporting tools and analytical methods • Manage the IT provider Next decision each month to maintain and upgrade the SQL database and web application through scrum method. (Steering committee done every 3 months with updated data roadmap and monthly performance review) • Creation of the technical reference document within Air Liquide Group regarding the Physical Data model of the database (66 slides of technical documentation and links between all the databases in LI Business Line) • AWS Athena SQL queries linking the industrial database and the maintenance database in order to build new Power BI and leverage data on two different database (SQL Server on Premise and AWS Athena Database) • Project management to bring the on premise database to cloud database (Azure and AWS + helping construct the future ETL and AWS parquet for the industrial database)