À propos de Victor
Lead Data Engineer | Data Analyst
I design and operate end-to-end data infrastructure from raw ingestion to business-facing insights for organizations that need reliable, scalable data foundations.
I build data pipelines and Lakehouse architectures (Bronze/Silver/Gold) on AWS, GCP and Azure, using dbt, Airflow and cloud-native warehouses including Snowflake, BigQuery, Microsoft Fabric and DuckDB.
On the delivery side, I turn raw data into actionable KPIs and dashboards via Power BI, Looker and Tableau, and ship predictive models for segmentation, forecasting and anomaly detection directly into business workflows.
What sets my work apart:
- Fast grasp of business context, so technical decisions stay aligned with what actually matters to stakeholders
- Production discipline by default: testing, versioning, CI/CD and documentation, not afterthoughts
- DevOps/DataOps practices applied to data engineering, for pipelines that don't break silently
- Comfortable both building from zero on greenfield projects and untangling existing stacks with technical debt
I work across maturity levels: laying the first foundations of an analytics infrastructure, or taking ownership of an established stack and making it more reliable, more automated, and easier to extend.
Goal: turn your data into a measurable performance lever, with solutions built for your actual constraints and delivered fast.
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 50 km), Lyon (jusqu’à 15 km), Lille (jusqu’à 10 km), Marseille (jusqu’à 50 km), Bordeaux (jusqu’à 30 km)
Expériences
- Novo NordiskData Architect | EngineerINDUSTRIE PHARMACEUTIQUEavril 2026 - Aujourd'hui (4 mois)Paris, FranceObjective: Joined as sole external data architect/engineer to design and deliver a secure, AI-ready data integration pipeline for pharmaceutical commercial data (sell-in), replacing a legacy SFTP/Excel-based vendor process with a governed Snowflake architecture.Data Architecture & Ingestion
- Designing and deploying a vendor deposit layer on AWS Transfer Family (SFTP-compatible) + S3, replacing manual FileZilla transfers with versioned, bucket-isolated storage per vendor
- Building event-driven ingestion into Snowflake via Snowpipe (SQS-triggered on S3 PUT)
Analytics Engineering & Data Modelling- Structuring a layered Snowflake architecture (RAW → TRUSTED → ANALYTICS) via dbt, with star schema fact/dimension tables covering UGA geographic aggregation, CIP/UN/UNEQ product hierarchies and GERS/IQVIA sell-in feeds
- Building a pre-aggregated ANALYTICS serving layer for Power BI, fully dbt-documented for lineage and future ML use cases
Operational Reliability & Governance- Setting up DEV/PROD environment isolation (Snowflake + dbt target environments)
- Delivering operational runbook, data dictionary and vendor onboarding guide to ensure full internal autonomy
Impact: Replacing an undocumented, manual vendor-deposit process with a secure, event-driven, AI-ready data platform serving rare disease and cardiometabolic commercial reporting.Tech Stack: AWS S3, AWS Transfer Family, AWS IAM, AWS SQS, Snowflake, Snowpipe, dbt, Power BI, Python, SQL - AssoConnectSenior Analytics EngineerEDITION DE LOGICIELSavril 2026 - Aujourd'hui (4 mois)Paris, FranceObjective: Joined as sole freelance analytics engineer to stabilize and extend a production dbt stack serving a French SaaS platform for nonprofit management, with no prior documentation or monitoring in place.Analytics Engineering
- Audited and refactored a 667-model dbt project, identifying and resolving critical data quality issues affecting financial metrics and client segmentation
- Extended the transformation layer to support a third market and brand, ensuring consistency across all downstream models and dashboards
- Delivered custom analytics models for enterprise network clients, isolated in a dedicated dbt layer
Operational Reliability- Designed and shipped automated pipeline monitoring with Slack alerting, replacing a fully manual and opaque operational model
- Established recovery patterns for incremental models after pipeline failures, reducing data gaps and manual intervention
- Surfaced and resolved silent failures across ingestion, transformation and scheduling layers
AI-Augmented Engineering- Local multi-agent setup (Claude Code + Ruflo + 22 specialized agents) for codebase audits, SQL debugging and dbt model generation
- Used agentic workflows to accelerate root-cause analysis and reduce time-to-fix on complex pipeline issues
Impact: Turned an undocumented, failure-prone stack into a reliable foundation for Finance, Ops and Partnerships reporting across 3 brands and markets.Tech Stack: dbt, BigQuery, Popsink, Fivetran, RudderStack, GitHub Actions, Metabase, GCP, SQL, Python, Claude Code, Ruflo - QuitoqueLead Data Engineer | Data AnalystLOGISTIQUE & SUPPLY CHAINmars 2026 - Aujourd'hui (5 mois)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- Built a local multi-agent AI system (Claude Code orchestrated via Ruflo, 22 specialized agents) to parallelize codebase audits, SQL debugging, dbt model generation and PR reviews
- Each agent runs a focused role (researcher, security, code-reviewer, perf-sentinel...) coordinated through a shared task queue, cutting audit and review cycles from hours to minutes
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
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Formations
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- Data Analyst - RNCP 6, Traitement des donnéesOpenClassrooms2021Data Analyst - RNCP 6, Traitement des données
Certifications
- Data analystOpenClassrooms2021