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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Solution Architect - **Company:** Data & Analytics - **Location:** Belgium - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Amazon S3, Computing Platforms, Audit Trail, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Warehousing, DevOps, Identity and Access Management, Key Management, Machine Learning, Metadata, Oracle (Applications), Role-Based Access Control, Reliability Engineering, Data Mesh, DataOps, Systems Integration, Enterprise Data Management, Data Logging, Amazon Virtual Private Cloud (VPC), Data Lakes, AI Platforms, Information Technology, Data Analytics, Machine Learning Operations, Terraform - **Published:** November 11, 2026 - **Apply:** https://www.3d-ict.be/vacatures/solution-architect-applications/ ## About the Role * Master's degree in Computer Science, Information Technology, Engineering or a related field, or equivalent through relevant professional experience. * At least 8 years of relevant experience in data, cloud or platform engineering, including at least 3 years in a solution or platform architecture role. * Proven experience designing data and analytics platforms on AWS and integrating them with on-premises environments. * Strong knowledge of AWS architecture, preferably including IAM, VPC/networking, S3, Redshift, SageMaker, monitoring and security services. * Demonstrated experience with data mesh, data products, modern data stacks, data warehouses, data lakes/lakehouses, metadata, cataloguing and lineage. * Experience with ingestion, CDC, ETL/ELT and orchestration patterns. Knowledge of Oracle GoldenGate, BODS, dbt or comparable technologies is an asset. * Expert understanding of Infrastructure as Code and CI/CD. Practical experience with Terraform or equivalent tooling is strongly preferred. * Strong understanding of DevOps, DataOps, MLOps, SRE and FinOps principles in enterprise data environments. * Experience designing security and access-control patterns for highly sensitive data and regulated environments. * Ability to translate strategic direction into phased target architectures, implementable roadmaps and concrete engineering standards. * Excellent stakeholder management, facilitation, coaching, problem-solving and communication skills. * Professional proficiency in English and fluency in Dutch or French, with passive knowledge of the other national language. Expected Deliverable Current-state and target-state architecture views for the data platform ecosystem. A phased architecture roadmap for EDWH modernization, EDP evolution and data science/AI platform ## Description (Cloud) Solution Architect, The architect owns the technical coherence and engineering guardrails for the modern data and analytics platform ecosystem. The role ensures that EDP, data science and AI capabilities, and data warehouse modernization evolve as one secure, reusable and manageable landscape rather than as separate technology islands. Job mission Own and evolve the solution architecture for client's data and analytics platform ecosystem. Translate business, data, risk and regulatory requirements into implementable architecture, reusable platform capabilities and clear engineering guardrails. Guide infrastructure and platform teams during implementation and coach stream-aligned data teams in the correct use of those capabilities. Key Responsibilities 1. Platform Architecture Design and maintain coherent target and transition architectures across AWS, EDP, data science and AI platforms, the enterprise data warehouse and required on-premises integrations. Define reusable architecture patterns for compute, storage, networking, identity, observability, environments, workspaces and platform services. Assess new initiatives and technologies, including Amazon SageMaker, S3, Redshift and related AWS services, and determine how they integrate with the wider client's platform landscape. Prevent unnecessary platform duplication by distinguishing business capabilities from reusable technical platform capabilities. 2. Modern data architecture and EDWH modernization Direct the evolution from classical data warehouse technologies toward cloud-native and mesh- based implementations using data products and clear data contracts. Define patterns for source-aligned and consumer-aligned data products, ingestion, CDC, ETL/ELT, orchestration, semantic models, metadata, cataloguing and lineage. Guide phased and hybrid migration choices, including coexistence between on-premises and cloud components, continuity, rollback and decommissioning. Evaluate modernization options for legacy components and integration patterns, including BODS, Oracle GoldenGate/CDC, dbt and modern orchestration technologies. 3. Platform engineering and automation Define Infrastructure as Code, CI/CD and environment management standards for infrastructure, data engineering, MLOps and ML engineering. Work with infrastructure and platform teams to create reusable templates and automated provisioning for accounts, workspaces, storage, compute, roles, policies, pipelines, logging and monitoring. Enable self-service for stream-aligned teams while preserving central architecture, security and operational guardrails. Coach teams in DevOps, DataOps, MLOps, Site Reliability Engineering and FinOps practices. 4. Security, risk and compliance by design Design security patterns for sensitive healthcare and member data, including IAM, RBAC, RLS, CLS, segregation of duties, encryption, secrets management, auditability and traceability. Ensure alignment with client's standards and applicable requirements for privacy, security, risk, legal, retention and regulatory compliance. Define secure hybrid connectivity patterns across AWS, on-premises environments, Direct Connect, firewalls and network zones. 5. Architecture governance and collaboration Act as the technical bridge between Data & Analytics, Galileo/infrastructure, network, security, risk, legal, enterprise architecture and business domains. Coach platform engineers, data engineers, data scientists and ML engineers and ensure implementations remain aligned with approved architecture principles. Document decisions through architecture views, standards, reference architectures, roadmaps and Architecture Decision Records. Support architecture reviews, estimates, vendor evaluations, incident analysis and root-cause investigations. 6. Reliability, observability and cost Define monitoring, data quality, performance, availability, capacity and cost-management requirements across the end-to-end platform. Identify opportunities to improve scalability, resilience, operability and cost transparency through