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I am a Data and Cloud Solutions Architect with more than 10 years of industry experience, complemented by an academic and scientific background in physics.

My career spans enterprise architecture, data governance, data engineering, software architecture, cloud platforms, data science, financial and actuarial systems, and applied AI.

I work at the intersection of strategy and implementation: translating complex business, mathematical, regulatory and technical requirements into reliable, scalable and maintainable platforms.

Throughout my career, I have led cross-functional and internationally distributed teams while remaining hands-on in architecture validation, proofs of concept, platform design, data engineering and technical governance.

• Enterprise, solution and data architecture
• Technology strategy and platform modernization
• TOGAF and DAMA-aligned architecture practices
• Architecture assessments, roadmaps and decision frameworks
• Cloud and platform standardization
• Business capability and technology alignment
• Cross-domain stakeholder and vendor coordination
• Cost, risk and architectural trade-off analysis

• Enterprise data governance operating models
• Data catalog, metadata management and business glossary
• Data discovery, inventory and cross-domain search
• Technical and business lineage
• Data ownership, stewardship and accountability
• Data marketplace and self-service data capabilities
• Data quality, lifecycle and policy management
• Access governance and granular permission models
• GDPR and ISO 27001-aligned controls
• Governance across data platforms, applications and analytical products

• Governance platform discovery and capability assessment
• Vendor benchmarking and structured comparison
• RACI, risk and weighted scoring matrices
• Proof-of-concept definition and evaluation
• Test scenarios and acceptance criteria
• Platform selection and adoption roadmaps
• Migration and phased rollout strategies
• Integration with cloud data and analytical ecosystems

• Atlan
• Alation
• DataHub and Acryl
• Zeenea
• Alex Solutions
• Collibra
• Dataiku
• Other enterprise catalog, metadata and governance platforms

• Data lakes, lakehouse and distributed data platforms
• Batch, streaming and event-driven architectures
• Data modeling, integration and transformation
• Metadata-driven orchestration
• Data quality and deterministic validation
• Observability and operational monitoring
• APIs, backend services and integration architecture
• Proofs of concept and technical feasibility validation

• AWS, Microsoft Azure and Google Cloud Platform
• Microsoft Fabric, Databricks, Snowflake and Apache Spark
• Kafka and real-time data processing
• Kubernetes, Docker and Terraform
• Infrastructure as Code and environment automation
• CI/CD, release strategy and versioning
• Development, test and production platform design
• Platform reliability and cost optimization

• Machine learning and predictive analytics
• Retrieval-augmented generation
• Vector search and knowledge retrieval
• AI-assisted engineering and DevSecOps
• Model ownership and lifecycle controls
• Documentation, peer review and validation standards
• Explainability and non-black-box governance
• Local and privacy-preserving AI tooling

I approach platform and governance decisions as a structured lifecycle:

1. Understand business capabilities, regulatory requirements and current constraints.
2. Define architectural principles and measurable quality attributes.
3. Assess platform capabilities using evidence-based comparison criteria.
4. Establish ownership, stewardship and decision responsibilities.
5. Validate critical assumptions through proofs of concept.
6. Design adoption and migration roadmaps with controlled rollout stages.
7. Integrate governance into engineering and operational workflows.
8. Measure platform quality, adoption, cost, reliability and business value.

• Connect architecture decisions to measurable business and operational needs.
• Treat governance, security, quality and observability as architecture concerns.
• Prefer simple, explicit and maintainable solutions over unnecessary complexity.
• Validate important decisions through evidence, benchmarks and proofs of concept.
• Design platforms for controlled evolution rather than one-time delivery.
• Automate testing, validation, deployment and operational checks.
• Document architectural decisions, assumptions and trade-offs.
• Build reusable public references without exposing proprietary implementations.

My technical perspective has been shaped by work across:

• Enterprise cloud and data platform architecture
• Data governance and metadata platform assessment
• Distributed data and real-time systems
• Financial and actuarial platforms
• Scientific and mathematical computing
• Data science and machine learning
• Software and backend engineering
• Organizational leadership and engineering enablement

This combination allows me to move between executive objectives, governance operating models, architectural decisions and implementation-level technical details.

The repositories published here contain independent projects, experiments, educational applications and reusable reference implementations.

They use synthetic, public or independently created examples and do not represent proprietary customer systems, employer source code, internal packages or confidential business implementations.