Job Description: Lead Platform Engineer Type of contract: Freelance Yearly B2B contract Location: Brussels-Haren (Hybrid-Once a week min) Role Summary: We are looking for an experienced and hands-on Lead Data Platform Engineer to architect implement, and lead the delivery of a modern, self-service data platform, guided by the Data Mesh paradigm. This role is critical to enabling domain teams to build, deploy, and manage high-quality data products independently, enabling decentralized data ownership, and ensuring scalability, governance, and efficiency across the data ecosystem. You will serve as the technical lead, translator of needs, and strategic planner—bridging the gap between data product teams’ pain points and platform capabilities. This role requires deep technical expertise in DevSecOps practices, Azure Cloud, Databricks, and modern data engineering practices. Proven experience in building such platforms is essential. Key Responsibilities: Architect and implement the data platform infrastructure end-to-end, including data ingestion, processing, quality, lineage, cataloging, security, and observability layers. Define and execute a step-wise implementation roadmap for the self-service data platform—starting from core capabilities to advanced self-service tooling. Engage deeply with data product teams to understand their challenges and translate those into platform features and reusable solutions. Establish clear standards, patterns, and templates to accelerate the delivery of domain data products while enforcing consistency, governance, and quality. Prioritize and manage the platform team’s backlog, balancing business impact, scalability, and feedback from users. Champion and implement the four core Data Mesh principles : domain-oriented ownership, data as a product, self-serve platform, and federated governance. Implement robust security, privacy, and access control mechanisms (e.G., Unity Catalog, Role-Based Access Control) to protect sensitive data. Drive the development and automation of onboarding processes for new domains and data products. Lead architectural design reviews, technical roadmap planning, and hands-on engineering work with the team. Mentor and guide data engineers working on platform and infrastructure components. Expertise and Skillset Required: Technical Skills: Proven experience architecting and building scalable, cloud-native data platforms on Azure Cloud and Databricks. Deep understanding of modern data architecture patterns (e.G., Lakehouse, Data Mesh, Data-as-a-Product). Deep knowledge of Azure components: Data Lake Storage Gen2, Data Factory, Synapse, Event Hubs, CosmosDB, Azure ML etc. Hands-on expertise in Databricks Lakehouse Platform, including Delta Lake, Unity Catalog, DBX, Spark optimisation, notebooks, jobs, and SQL endpoints. Proven expertise in setting-up reproducible platform using Infrastructure as Code (Terraform) Strong programming and scripting skills in Python and SQL; experience with Spark is essential. Experience integrating CI/CD pipelines for data products using tools like Azure DevOps or GitHub Actions. Platform Design & Data Mesh Thinking: Experience implementing the four principles of Data Mesh in real-world environments. Expertise in designing data contracts, access patterns, and versioning mechanisms for data products. Understanding of metadata management, cataloging, and governance frameworks (e.G., Collibra, Unity Catalog, Azure Purview). Platform Leadership: Ability to define phased implementation roadmaps (e.G., MVP > scalable core services > domain onboarding frameworks). Experience leading and mentoring platform engineers, shaping best practices, and driving architectural decisions. Effective at leading a platform engineering team, conducting reviews, and ensuring delivery quality. Proven capability in backlog management, agile delivery, and prioritization based on user feedback and business impact. Stakeholder Engagement: Strong interpersonal and communication skills to collaborate across domains and disciplines. Ability to facilitate workshops and feedback sessions with data product teams and convert insights into technical requirements. Preferred Qualifications: Background in implementing data platforms in regulated or enterprise environments. Familiarity with dbt, Apache Airflow, or orchestration tools. Experience with observability and monitoring tools for data reliability (e.G., Monte Carlo, Great Expectations, Datadog).