This page makes the relationships explicit: how target-state architecture, data foundations, AI governance, hybrid cloud, delivery systems, and executive operating models fit together.
node 01
Enterprise Architecture
Target-state architecture, standards, portfolio rationalisation, transition roadmaps, and architecture governance for regulated enterprises.
Architecture principles and standardsReference architectures and paved-road patternsArchitecture Decision RecordsCapability and dependency mappingApplication lifecycle and technical debt scoringARB / Tech Council operating cadence
Operated enterprise architecture governance forums and advised CIO/CDO, security, audit, and risk stakeholders across Swiss and DACH financial institutions.
Questions this answers
- What should the target-state architecture look like?
- Which platforms and applications should be rationalised first?
- How do we make architecture decisions auditable?
node 02
Data & AI Architecture
Enterprise data platforms, AI/GenAI guardrails, AI-supported SDLC, lineage, quality, and architecture patterns for production AI.
Enterprise data strategyData governance and qualityData lineage and controls as evidenceAI/GenAI governanceModel risk liaisonLLMOps and AI-supported SDLC
Built the Swiss/DACH Data Engineering & ML practice from 0 to 60+ FTE and delivered regulated data platforms and AI governance work.
Questions this answers
- Is our data foundation ready for AI?
- Where should AI controls enter the SDLC?
- How do we prove lineage, quality, and accountability?
node 03
Governance, Risk & Evidence
Regulatory evidence, BCBS 239 alignment, audit remediation, security patterns, risk controls, and enterprise-grade documentation.
BCBS 239 remediationAudit findings remediationSecurity and operational risk patternsCompliance evidence packsException handlingControls as evidence
Led regulatory-aligned initiatives where architecture, data lineage, audit remediation, and controls had to hold up under scrutiny.
Questions this answers
- Can we defend this system to audit and risk stakeholders?
- Which controls need evidence, ownership, and cadence?
- What is the minimum governance model that still works?
node 04
Hybrid Cloud & Platforms
On-prem plus Azure architecture, identity and network patterns, Databricks/Delta Lake, IaC, CI/CD, observability, and platform guardrails.
Hybrid cloud architectureAzure platform servicesIdentity and access patternsDatabricks and Delta LakeTerraform / IaCCI/CD and observability
Architected regulated platform and integration patterns on hybrid cloud aligned with infrastructure, delivery, and governance constraints.
Questions this answers
- Which workloads belong on-prem, cloud, or hybrid?
- How do platform guardrails reduce delivery risk?
- How should identity, network, and data boundaries be designed?
node 05
Engineering Delivery
Delivery governance, SDLC quality gates, automation, Python/SQL/PySpark engineering, observability, and production reliability.
Delivery governanceQuality gatesAI-supported SDLCPython, SQL, PySparkMigration factoriesProduction observability
Established delivery standards and quality gates while running multi-team transformation programmes in regulated environments.
Questions this answers
- How do we turn architecture intent into delivery behavior?
- Which quality gates matter before production?
- How do we scale migration and modernisation work?
node 06
Leadership & Operating Model
Practice building, P&L ownership, executive stakeholder management, team design, mentoring, and operating models for data and AI teams.
Practice buildingP&L ownershipExecutive stakeholder managementTeam and role designMentoring architects and tech leadsProduct/agile-at-scale operating models
Owned a CHF 20M+ portfolio, sustained >85% utilisation, improved margin by >11%, and built teams across Switzerland and DACH.
Questions this answers
- What team shape is needed for enterprise data and AI?
- Which decisions should sit with architects, product, security, or risk?
- How do we build leadership cadence without bureaucracy?