About Data & Analytics

The Data & Analytics department within ICT supports client in becoming a leading data-driven and

proactive healthcare fund in Belgium. We guide business domains across the data and analytics

journey and deliver data strategy, governance, business intelligence, advanced analytics, data

science, AI and tailored data products. The multidisciplinary department operates both in run mode

and project mode and works closely with platform, infrastructure and business teams.

Client is evolving a complex landscape that combines the existing enterprise data warehouse, the

Enterprise Data Platform, data science and AI capabilities, and hybrid cloud and on-premises services.

To anchor architecture knowledge and guide this evolution, we are looking for an experienced Data

(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

tagging, chargeback/showback and FinOps practices.

Qualifications Wishlist

• 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

capabilities.

Reference architectures, platform guardrails, reusable patterns and Architecture Decision Records.

Security, identity, network, observability, continuity and FinOps requirements for platform

implementation.

Coaching and architecture support for Galileo/infrastructure and stream-aligned data teams.

Taalvereiste : Moedertaal NL of FR, passieve kennis van de andere landstaal en professionele kennis

van Engels.