Regulated Institutional Systems
Architecture and analysis support for long-lived institutional environments with strong governance, auditability, and stakeholder coordination needs.
Summary
Architecture, traceability, and AI-supported analysis for complex systems.
More than 25 years in IT, business analysis, enterprise architecture, and delivery, now focused on institutional systems, requirements traceability, and responsible AI-supported sensemaking.
Experience profile
Capability profile across architecture, analysis, delivery, and institutional change.
- 25+ years in IT, enterprise transformation, consulting delivery, and complex systems work.
- 20+ years across business analysis, requirements engineering, IT service management, and process improvement.
- 17+ years applying business and enterprise architecture to bridge strategy, operating models, data, and implementation.
- 15+ years in project, product, delivery, and practice leadership with distributed stakeholders and multidisciplinary teams.
- Recent focus on regulated institutional systems, statistical and data domains, traceability, and legacy system readability.
- Current research direction: responsible AI-supported sensemaking, requirements traceability, and human-centered orientation.
Profile
Capability evolution across systems and domains.
My work began close to operations: support, service quality, internal systems, and the practical realities of keeping business-critical technology understandable for the people who depend on it.
From there, the focus moved into business analysis, requirements engineering, process improvement, and consulting delivery. The recurring problem was not only building systems, but preserving the logic behind them.
As responsibilities expanded, architecture became the natural center of the work: connecting strategy, operating models, data, governance, delivery, and long-term maintainability.
Recent work extends this foundation into European institutional, statistical, patent, and regulated-system contexts, where traceability, semantic consistency, and evidence-based change matter as much as implementation.
A current research direction connects architecture and analysis practices with responsible AI-supported sensemaking: helping people orient inside complex knowledge spaces without replacing human judgment.
The common thread is coherence. I help complex organizations make their systems, requirements, decisions, and data more legible, traceable, and resilient.
Recent focus
Where the work has become sharper in recent years.
A concise view of current capability areas, framed by domains, methods, and the kind of complexity they help resolve.
Statistical and Data Domains
Work across statistical, analytical, and data-source landscapes where definitions, lineage, quality, and domain meaning must be explicit.
Legacy Reverse Engineering
Recovery of undocumented business logic, data flows, system dependencies, SQL scripts, reports, and integration behavior.
Model-Driven Analysis
Use of ARIS, BPMN, UML, capability views, entity models, interaction models, and state models to make complex change easier to reason about.
Requirements Governance
Requirements templates, Jira and Confluence practices, UAT support, RTM automation, and quality-oriented delivery structures.
AI-Supported Traceability
Responsible AI use cases for requirement drafting, semantic linking, knowledge reuse, impact analysis, and explainable human-in-the-loop review.
Credentials and methods
Practical signals behind the architecture and analysis work.
Location and Authorization
- Luxembourg based
- EU long-term residence permit
- Open to relocation
Languages
- English C1
- Russian native
- Luxembourgish A2.1
- German A1
Methods
- TOGAF and ArchiMate applied
- BABOK-aligned business analysis
- ITIL
- Agile, Scrum, and Kanban
AI and Data Credentials
- NIST AI RMF Masterclass
- AI Business Leadership
- SAS Viya basics
- Data storytelling and visualization
Research and frameworks
Independent work on orientation, coherence, and Guiding AI.
A research layer exploring how people maintain orientation, traceability, and coherence in complex knowledge environments.
Independent assessment
Strengths highlighted in an external talent assessment.
Decision Making
Proficiency in making high-quality decisions in complex delivery and consulting contexts.
Innovation
Strong ability to generate creative approaches and improve business and technology processes.
Leadership
Experience building teams, practices, and operating structures for large-scale technology delivery.
Skills
Hard and soft skills overview.
Key hard skills
- Enterprise and business architecture
- Business analysis and requirements engineering
- Requirements traceability and RTM
- IT service management and governance
- Data, BI, and statistical platform analysis
- Legacy system and data-flow reverse engineering
- AI-supported analysis and knowledge reuse
Key soft skills
- Facilitation and workshop leadership
- Stakeholder alignment across business and IT
- Structured decision making
- Leadership and mentoring
- Clear executive communication
- Calm orientation in ambiguous environments