Diego Negrier

• Implementation and structuring of a data catalog to centralize, inventory, and facilitate access to the company's data resources.

Conception and continuous enrichment of a data dictionary enabling better governance and understanding of information assets.

Development and implementation of data quality management processes, including automated controls, validation rules, and quality indicators.

Definition and implementation of scalable data architecture models, ensuring the performance, security, and scalability of information systems.

Within the marketing department, my missions aimed to improve marketing products and centralize data from different databases to create added value. The strong challenge of this collaboration was to support the marketing department in its growth objectives by enabling it to better know its targets (readers), retain them, offer new products, and recruit new customers.

Data Architect

Implementation of a scalable and flexible data architecture based on Google Cloud Platform (GCP). Development of data processing pipelines in Python to continuously integrate new data sources, thus feeding a centralized DATA warehouse.

Data Analyst

By leveraging this enriched data, development of predictive models to segment customers and personalize marketing campaigns. Creation of interactive dashboards (Looker) to track campaign performance and measure the impact of actions taken. Implementation of a personalized recommendation engine (Flask) to offer targeted content to readers via the media's website and also to business users via an application.

My main mission was to set up a data environment to process local data, detect anomalies, and support business units in their decision-making with ready-to-use tools. The evolution of pension scheme regulations impacts data management and complicates the understanding and consistency of this data.

Data Governance

Definition and creation of a data quality management methodology (FMEA).

Definition of data quality processes (consistency, integrity, prioritization).

Change management: organizing meetings between business units and the data quality department, explaining the roadmap to the teams.

Reference data management: creation of a methodology including the data dictionary, data mapping.

Human and technical challenges: identification of human and technical challenges related to change management.

Data Architect

Data architecture design: upstream thinking about data modeling (what data, what sources, what format).

Modeling of different data sources: creation of a scalable table

Data Analyst

Data analysis: identification of optimization levers for processing data anomalies (Pareto method).

Creation of statistical and graphical notebooks for data frames, notebooks for analyzing overlapping dates.

Use of analytical tools: creation of tools adapted to the technical profiles of collaborators - use of SQL (Hadoop) for data analysis.

Business support: creation of documentation adapted to users with varied but non-technical profiles.

Creation of Flask applications to display key indicators.