Sven Koolen

As Lead Data Engineer at Scidentify, I built a cloud-native data platform on Azure and Databricks to analyze high-dimensional mass cytometry data. I developed signal processing and morphological analysis algorithms, designed ETL pipelines to process 100+ biomedical datasets, and enabled high-throughput workflows for cancer research and diagnostics. I also led product development across infrastructure, data pipelines, and FastAPI services, implementing CI/CD and Infrastructure-as-Code with Azure DevOps and GitHub Actions. This role combined deep technical execution with platform architecture, transforming raw biomedical signals into actionable insights.

As owner of Betastream, I worked as a Freelance Consultant with a focus on data engineering, cloud infrastructure, and MLOps. A key project was the Data Platform (DP), a cloud-native Azure platform that centralized and optimized enterprise data.

As Data Engineer & Infra Lead (±2.5 years), I deployed Azure resources (Synapse, Data Lake, Functions, Data Factory) with Infrastructure as Code, enforced enterprise security, and built ETL pipelines for structured and unstructured data. I co-designed dimensional models in Delta/Parquet to power Power BI and Cosmos DB, applied Medallion Architecture for scalable data layers, and delivered 20+ serverless REST APIs. I also led the Infra team and worked closely with business and IT in an agile model.

Earlier, as MLOps Engineer (±6 months), I productionized ML PoCs by integrating AzureML into Gasunie’s landscape. I built CI/CD-enabled MLOps pipelines for training, deployment, and monitoring, successfully operationalizing two ML models and ensuring reproducibility and business value.

Through Betastream, I combine robust data engineering with applied machine learning, delivering secure, scalable, and business-driven solutions.

At Accenture Applied Intelligence, I worked as a Data Scientist and Data Engineer across multiple international projects (Dutch and Greek offices), delivering end-to-end AI, ML, and data solutions for clients in mining, energy, healthcare, and utilities. My role combined advanced analytics, machine learning, and scalable data engineering, always with a strong business focus to generate measurable value.

For a South African mining company, I built a predictive maintenance platform to forecast roof failures in deep-shaft mines. This included analyzing geological and operational data, designing and deploying three ML models (two supervised, one unsupervised) in AzureML, and integrating them with Delta Lake and Blob Storage for real-time insights. I also implemented MLOps pipelines with monitoring and explainability, plus CI/CD workflows using Azure DevOps to ensure reliable production deployment.

For a Dutch network operator, I turned operational Mendix data into actionable strategic insights. I created a one-year BI roadmap, set up a new data team, and delivered multiple PowerBI dashboards for regional OpCo’s. This required data modeling, supervised ML, and close collaboration with stakeholders to align data strategy with business priorities.