About Wilson
Welcome to my profile!
Companies invest heavily in Data, BI and GenAI products — but those investments only pay off on a reliable, well-modeled and documented data layer. That's where I come in: I build and harden the analytics layer that turns raw, multi-source data into trusted, decision-ready metrics.
With experience across sector leaders (Sanofi, LVMH, Bouygues, Nestlé), I work at the intersection of Analytics Engineering, BI and Product. I'm at my best where a BI or GenAI product needs a trustworthy data foundation built — or rescued — from modeling and pipelines to the dashboards leadership relies on.
Recently embedded in Sanofi's GenAI team, I designed and industrialized the data & analytics layer behind Concierge, an enterprise-scale conversational LLM assistant: usage-data modeling, tested and version-controlled pipelines, and product-monitoring dashboards.
🎯 What I bring:
- Analytics Engineering — data modeling (staging → marts), grain management, deduplication, robust SQL transformations, version-controlled (Git) and tested.
- Data Quality & Reliability — quality frameworks, automated testing, documentation and lineage for trustworthy production metrics.
- BI & Dataviz — actionable dashboards (Power BI, Looker Studio) built for decisions, not just reporting.
- GenAI & Product Analytics — LLM evaluation, RAG performance, agent-routing accuracy, AI observability, adoption metrics.
🛠️ Stack: SQL · Python · dbt · BigQuery · Snowflake · Power BI · Looker Studio · Dataiku · Git/CI-CD · ETL/ELT · LLM Evaluation · RAG · Prompt Engineering · AI Agents
💡 My approach: I combine data-engineering rigor (modeling, testing, versioning, documentation) with genuine product thinking. I don't just deliver raw numbers — I build the reliable layer that produces them, then translate them into clear, actionable recommendations.
Ready to harden and industrialize your data, BI or GenAI layer? Let's talk!
French
Native or bilingual
English
Native or bilingual
Can work on-site
Paris (up to 50km), Bordeaux (up to 50km), Lyon (up to 50km), Marseille (up to 50km)
Experience
- SanofiAnalytics Engineer / Product Data AnalystHEALTH AND WELLNESSMay 2025 - Today (1 year and 3 months)Paris, FranceMember of the core team building Concierge, Sanofi's enterprise GenAI assistant — a multi-agent conversational platform connected to Microsoft Graph, ServiceNow, SharePoint and specialized LLM agents (Clinical Studies, Brand, Web, Support).I own the data layer behind product decisions, from usage-data modeling to monitoring dashboards.
- Data Modeling & Transformation — Designed layered models (staging → marts) on BigQuery, unifying GA4 events, LLM logs, telemetry and AmazonDB, with a clear split between front-end behavioral data and back-end operational events.
- Canonical Entity Resolution — Built a canonical registry collapsing 129 agent-ID variants into 6 reference entities, restoring trustworthy per-agent analytics across the whole reporting suite.
- Grain Management & Deduplication — Resolved grain mismatches in Web/App adoption tables and fixed double-counting from a new two-event GA4 pattern, deduplicating in the transformation layer.
- Data Quality & Industrialization — Automated QA frameworks, version-controlled (Git) and documented transformations, cross-region dataset handling (europe-west1), and a Power BI incremental-refresh fix that ended row duplication.
- Source & Architecture Evaluation — Assessed Datadog vs GA4 as event sources routed into Snowflake, and contributed to the Informatica IDMC ↔ Snowflake integration (Key Pair Authentication, Secure Agent, iPaaS GitHub repo).
- BI & Monitoring — Power BI and Looker Studio dashboards tracking adoption, agent usage, retention, latency, token usage and agent success rate.
- LLM & Agent Evaluation — Scorecards for response quality, agent-routing accuracy, hallucination rate and RAG performance, feeding prompt-engineering and routing iterations.
Stack: SQL · Python · BigQuery · Snowflake · dbt · Power BI · Looker Studio · GA4 · Datadog · Informatica · Git/CI-CD · LLM Evaluation · RAG - BOUYGUES TELECOMSenior Data AnalystTELECOMMUNICATIONSOctober 2024 - Today (1 year and 10 months)Meudon, FranceDeployed a modern analytics ecosystem (Tableau, SQL, Google Cloud Platform) making data-driven insights accessible and actionable for 50+ stakeholders across all levels of the organization.
- Data Infrastructure on GCP — Engineered and maintained scalable data solutions on Google Cloud Platform, designing a secure, flexible foundation that adapted to shifting business priorities and gave teams reliable, self-served access to insights.
- Dashboard Design & Modeling — Partnered with diverse teams to build interactive Tableau dashboards tailored to each audience, modeling the underlying data and standardizing metric definitions so stakeholders could spot trends, monitor performance and align strategy without ambiguity over the numbers.
- Data Reliability — Introduced data-quality and QA frameworks ensuring consistent, trustworthy outputs, reducing back-and-forth over figures and giving leadership confidence in the data behind their decisions.
- Strategic Analysis — Ran deep-dive analyses of key metrics with top management, translating complex datasets into clear recommendations supporting growth plans and daily operations.
Stack: SQL · Google Cloud Platform · Tableau · Python - Nespresso FranceData Analyst B2BAGRICULTURESeptember 2023 - August 2024 (1 year)Issy-les-Moulineaux, FranceProvided data and insights to over 50 stakeholders, primarily in France and across the European zone, by building a robust analytics infrastructure that supported real-time decision-making and data trust across departments.
- Directed the creation and continuous optimization of PowerBI dashboards, leveraging advanced data visualization techniques to provide actionable insights to both local and international teams, enhancing decision-making processes across regions
- Designed and maintained complex databases, establishing robust data architecture to support evolving business needs and facilitate efficient data access for stakeholders
- Led data trust initiatives by implementing rigorous quality assurance processes to ensure the accuracy and reliability of data, enabling precise analyses that inform critical business decisions
- Conducted in-depth strategic analyses of customer data in collaboration with top management, utilizing insights to develop growth strategies and refine cross-regional initiatives in alignment with broader organizational goals
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Education
- Master Grande ÉcoleNEOMA BS2020Spécialisation Marketing Digital
Certifications
- Hands-On Essentials: Data Warehousing WorkshopSnowflake2024