About Victor
Senior Analytics Engineer | Data Analyst
Expert en transformation des données en actifs stratégiques pour les organisations, je conçois et implémente des architectures data modernes, intégrées et évolutives.
Mon expertise
Je combine data engineering, analytics et data science appliquée pour créer des solutions concrètes à fort impact business :
- Architectures data modernes : Conception de pipelines robustes et évolutifs sur AWS, GCP et Azure en utilisant des frameworks comme dbt, Airflow et les technologies cloud natives.
- Modélisation analytique avancée : Création de structures de données optimisées (Lakehouse, approche Bronze/Silver/Gold) sur Snowflake, BigQuery, Fabric ou DuckDB pour garantir performances et fiabilité.
- Insights métier actionnables : Transformation de données complexes en visualisations percutantes et KPIs stratégiques via Power BI, Looker ou Tableau.
- Industrialisation de l'intelligence data : Développement et déploiement de modèles prédictifs intégrés aux workflows métier pour la segmentation, le forecast, la détection d'anomalies et l'optimisation.
Ma valeur ajoutée
Mes interventions se distinguent par :
- Une compréhension rapide des enjeux métier pour orienter les solutions techniques
- L'automatisation systématique des processus pour garantir fiabilité et scalabilité
- Une approche DevOps/DataOps avec versioning, CI/CD et documentation
- La capacité à traduire des besoins métier complexes en architectures data cohérentes
Je peux intervenir aussi bien dans des environnements data matures que sur des projets nécessitant de poser les fondations d'une infrastructure analytique.
Mon objectif : transformer vos données en levier de performance mesurable, avec des solutions adaptées à vos enjeux spécifiques et une mise en œuvre rapide et efficace.
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 50km), Lyon (up to 15km), Lille (up to 10km), Marseille (up to 50km), Bordeaux (up to 30km)
Experience
- QuitoqueLead Data Engineer | Data AnalystLOGISTICS AND SUPPLY CHAINMarch 2026 - Today (4 months)Paris 13 Gobelins, FranceObjective: Joined as sole data owner to own and evolve an existing analytics stack end-to-end, while supporting the broader Quitoque data team post-acquisition.Full-Stack Data Ownership
- Owned the entire data pipeline solo: Airbyte Cloud ingestion, BigQuery, dbt transformations, Metabase BI delivery and reverse ETL via Hightouch
- Single point of contact between business stakeholders (Marketing, Ops, Finance, CEO) and the data stack
Operational Intelligence- Built real-time Grafana dashboards on Speed WMS for live warehouse visibility: picking performance, stock levels, logistics flows
- Replaced manual processes (Google Sheets, ad-hoc queries) with self-serve dashboards used daily by buyers, logistics managers and ops leads
Analytics Engineering & Data Quality- Maintained and extended a 200+ model dbt project: bug fixes, new KPI models, incremental refactoring, data quality tests
- Fixed critical metric inconsistencies (churn rate, active base, marketing costs) by tracing root causes across the full pipeline
- Shipped a media spend model reconciling Facebook, TikTok and Google Ads at daily granularity
AI-Augmented Engineering- Local multi-agent setup (Claude Code + Ruflo + 22 specialized agents) for codebase audits, SQL debugging and dbt model generation
- Among the earliest practitioners of production-grade agentic workflows applied to data engineering
Impact: Gave BeneBono's ops, marketing and purchasing teams reliable, real-time data visibility across the supply chain, replacing manual processes and surfacing metric bugs that were distorting strategic decisions.Tech Stack: dbt, BigQuery, Airbyte Cloud, Hightouch, Metabase, Grafana, SQL Server, GCP, Postgres, Python, Git, Claude Code, Ruflo - EFOR GROUPSenior Data Engineer | Data AnalystPHARMACEUTICALS INDUSTRYApril 2025 - February 2026 (10 months)Paris, FranceObjective: Standardize healthcare operational data and build amodern analytics platform to support Finance and HR teams.Data Lakehouse Architecture & Modeling
- Designed and implemented a Lakehouse architecture on MicrosoftFabric, following the Bronze/Silver/Gold model.
- Developed robust data pipelines using PySpark, T-SQL, an SparkSQL to handle ingestion, cleaning, and modeling of multi-source data.
Dashboarding & BI Enablement- Built Power BI semantic models and dashboards tailored to
Finance and HR use cases.- Worked closely with business teams to ensure KPIs and aggregations matched operational reality.
Notebook Refactoring & Governance- Refactored legacy notebooks to improve modularity and
maintainability.- Helped enforce naming conventions and implement data quality
checks for stronger governance.Impact: Delivered a scalable and auditable data platform, enhancingoperational reporting and enabling faster, more reliable decision-making.Tech: Microsoft Fabric, Synapse, Power BI, PySpark, T-SQL, SparkSQL, Notebooks - QantevLead Data Engineer | Data AnalystBANKING AND INSURANCENovember 2024 - April 2025 (4 months)Paris, FranceObjective: Industrialize fraud detection and reconciliation workflows on healthcare claims data to support faster and more accurate investigations.Fraud Detection & Automation
- Led fraud detection initiatives, identifying suspicious reimbursement patterns and provider behaviors.
- Built scalable alerting pipelines in dbt, fully deployed on AWS with DuckDB for fast, auditable, and cost-efficient analytics.
Reconciliation & Data Matching- Developed advanced reconciliation logic across internal and external datasets.
- Reduced manual effort by automating data matching and validation workflows.
Cross-Team Collaboration & Advanced Analytics- Acted as the main interface between fraud ops, analysts, and engineers to align detection logic with business needs.
- Refined alerting strategies based on real-case investigations and contributed to improving fraud models accuracy.
Impact: Accelerated fraud detection, improved traceability, and empowered business teams with actionable alerts.Tech Stack: DBT, JupyterLab, AWS, Looker Studio, DuckDB.
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Education
- Ingénieur Machine Learning - RNCP 7, Traitement des donnéesOpenClassrooms2022Ingénieur Machine Learning - RNCP 7, Traitement des données
- Data Analyst - RNCP 6, Traitement des donnéesOpenClassrooms2021Data Analyst - RNCP 6, Traitement des données
Certifications
- Data analystOpenClassrooms2021