Rotterdam, South Holland, Netherlands
Enthusiastic and experienced Data Architect with expertise in data platform architecture and data management. Adept at designing and implementing data solutions aligned with business goals. Demonstrated success in advising, architecting, and implementing data platforms for leading organizations. Proven skills in technology innovation, metadata-driven processing, and data governance. Eager to contribute to cutting-edge projects that drive business success through data excellence. Key Achievements Innovative Data Platforms | Lakehouse Architecture | Azure and Databricks Solutions Expert | Data Governance Leadership
As architect I'm part of the triad in the ART Data at De Goudse (SAFe organisation) and responsible for four teams that develop data platform, data consumption, AI, ML and business process automation products and services. Main airchitectural epics are migration to a new data platform in MS Fabric, developing a new ML runway and connecting a new business line (with a data & AI first principle) to the platform.
In this position my responsibilities are designing the data models for several domains within the business, designing the master data model, leading the design and implementation of the data governance program and contributing to other domains within the EA capability. I also create reference and target architectures within projects with a data component, and give architectural guidance to the data & analytics platform team.
Architecture of the new Customer Data Platform at Louwman, replacing a number of existing solutions but also implementing new capabilities for customer profiling, marketing campaigns and analysis, with the goal to provide a centralized perspective on each customer. The architecture includes the integration with other solutions through an API layer and a master data management (MDM) solution. Applications are able to communicate with the solution in real time. The Platform is built on Azure with Databricks.
Solution Architect for the Model Data Factory (MDF), as part of the Future Model Landscape (FML) program in the Risk department. MDF is a platform that integrates, standardizes, cleanses, enriches and enables datasets for risk model development. My role involves creating and maintaining the solution vision, design platform functions, create solution directions to solve business problems and technical challenges. I'm also part of the project SDA in which I align with the other architects in the project and design the interoperability between MDF and other solutions. We made sure the architecture is compliant to regulatory requirements (European and National laws, ECB regulations and privacy). The platform is developed on Azure and has a lakehouse architecture. Pipelines are developed in Databricks using SQL and Python. Unity Catalog is utilized for governance, and Purview to capture end-to-end lineage and data cataloging. The reference- and metadata part of the platform was managed in EBX. Among the topics I focussed on within the platform are metadata driven processing, data quality, cleansing and unification engines, data governance, source ingestion patterns, data processing and storage patterns and the handshake with machine learning platforms.
Advising about and contributing to the architecture of the new Menzis Data Platform. I specifically worked on the following topics. - Databricks workload disaster recovery - Data Lake backup & restore - Creating diagrams of current and target architecture Technology and methods: Databricks, PySpark, SQL, Delta Lake medaillon structure, Azure Data Lake storage, Azure DevOps pipelines with Powershell and Yaml, Azure Backup Vault, GitHub.