About Jean-Christophe
Understanding => Clarity => Solution
👨💻 I take on complex technical projects: I quickly grasp the core of the problems, structure them during analysis, and then propose a set of pragmatic, robust and durable solutions. I help my client choose between options based on rational, objective and quantifiable data.
Method
Targeted diagnosis
- Clarify the problem statement.
- Divide and conquer: break the problem into subparts and study each one.
- Ecosystem: identify how the problem relates to other external systems.
Options evaluated pragmatically
Compare solutions based on total cost to build and own, maintainability, and operational impact.
Design focused on operations
Validate the analysis before implementation (proof) or by tests.
Handover
Documentation, tests and team training.
Technical capabilities (representative examples)
Trained as an aeronautical engineer, I have implemented technical solutions in demanding contexts that combine applied mathematics and computer science: process optimization for the energy sector, automation and modelling in regulated environments (Big Pharma), and development of secure distributed systems (blockchain). Technically, I regularly work on designing and industrializing data pipelines (Python, SQL, Spark), deploying ML/AI models to production, architecting distributed systems and optimizing compute, as well as software engineering and infrastructure (automated tests, CI/CD, containers, monitoring). I adapt tools quickly to the business context and to regulatory or operational constraints.
What I deliver
Clarity for decision making, operational pragmatism, verifiable reliability and team autonomy.
Contact me for a quick diagnostic (30 minutes) and an initial roadmap.
French
Native or bilingual
English
Fluent
Can work on-site
Bruxelles (up to 50km), Liège (up to 50km), Louvain-la-Neuve (up to 50km)
Experience
- ENGIEData EngineerApril 2024 - Today (2 years and 2 months)Experience Summary – Timeseries Architecture, Streaming Pipelines & Scalable Data EngineeringI design and optimise cloud‑native timeseries systems, streaming architectures and data ingestion frameworks that empower Data Scientists, Analysts and Business teams to deliver faster with fewer operational risks. My expertise spans AWS, Azure, Kafka, Delta Lake, Kimball modelling, scraper frameworks and performance optimisation.I designed a full Timeseries Storage System, from ingestion to Bronze/Silver/Gold layers, ensuring reliability, scalability and low‑latency access. I built a Scraping Framework that allows Data Scientists, Analysts and even non‑technical users to develop and deploy scrapers easily, dramatically reducing onboarding time and operational friction. I also trained and supported users to ensure smooth adoption and autonomous development.I implemented Kafka Consumers that ingest and structure timeseries datapoints into Delta Lake storage layers, following best practices for streaming, schema evolution and data quality. I developed a production‑grade scraper for the Meteomatics weather provider, integrating external APIs into the ingestion ecosystem.I contributed to the functional specifications of a Timeseries Metadata Management System, ensuring clarity, maintainability and long‑term extensibility. I also optimised the CPU and memory footprint of several system components, improving throughput and reducing cloud costs.My work combines cloud engineering, streaming architectures, data modelling and performance tuning to deliver robust, scalable and user‑friendly timeseries platforms.Keywords: AWS, Azure, Kafka, Delta Lake, Timeseries Storage, Scraping Framework, Data Engineering, Streaming Pipelines, Kimball Modelling, Bronze/Silver/Gold, Performance Optimisation, CPU/Memory Tuning, Metadata Management, Weather Data, Meteomatics, Cloud Architecture.
- V3 Engineering bvbaOwnerTECHJanuary 2011 - Today (15 years and 5 months)BelgiumData EngineeringData ScienceMachine learningBig PharmaDistributed ComputingHigh Performance ComputingApplied Science
- InsensSenior Data Scientist / Data EngineerApril 2023 - March 2024 (11 months)Experience Summary – High‑Performance Data Science Architecture, IoT Pipelines & Large‑Scale Cost OptimisationI design high‑performance, cloud‑native Data Science architectures for large‑scale IoT and industrial analytics products.I defined the full Data Science Architecture for the company’s flagship industrial product, operating under extremely tight performance and financial constraints. The system ingests data from thousands of IoT sensors, generating 5+ TB of data per month, requiring robust distributed processing and cost‑efficient storage strategies.I brought seniority to a team of young engineers through mentoring, coaching and hands‑on architectural guidance, raising engineering maturity and delivery quality. I developed and industrialised fault‑detection algorithms for industrial assets, ensuring reliability, reproducibility and production‑grade performance.I evaluated distributed database options, selecting the most scalable and cost‑effective solutions. I designed a highly parallelised and flexible framework capable of handling large‑scale inference and data processing workloads.I reduced AWS operational costs by two orders of magnitude, combining architectural redesign, storage optimisation, compute efficiency and Step Functions. I also developed advanced spectral‑analysis heuristics to determine the number of bars in squirrel‑cage rotors from engine current and voltage signals.Finally, I captured and formalised expert domain knowledge and integrated it into Data Science pipelines, ensuring long‑term maintainability and knowledge transfer.Keywords: AWS, High‑Performance Python, Metaflow, S3, Redshift, Step Functions, EventBridge, Celery, IoT, Distributed Systems, Predictive Maintenance, Fault Detection, Cost Optimisation, Timeseries, Spectral Analysis, Industrial Analytics, Data Science Architecture, Docker, Property‑Based Testing.
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Education
- Master of Science in BioinformaticsUniversité libre de Bruxelles2003Master, Bio-informatics
- MSc, Aerospace Vehicle DesignCranfield Institute of Technology1988MSc, Aerospace Vehicle Design
Certifications
- DB 110 - Apache Spark Tuning and Best PracticesDatabricks2020