Data & Integration Architecture
Turning fragmented information into usable enterprise capability
I design information flows and integration architectures that allow applications, platforms and organisations to exchange data securely, reliably and at scale — from logical data models and API contracts through event-driven systems and analytics platforms.
Architecture coverage
Data
- Information architecture & data flows
- Logical and physical data models
- Data classification & sensitivity tagging
- Data lifecycle, retention, residency, sovereignty
- Metadata, lineage & data quality
- Master & reference data management
- Data ownership & stewardship
Integration
- API-first architecture (REST, GraphQL, gRPC)
- API gateways · throttling · versioning
- Event-driven architecture (Kafka, AMQP)
- Azure Service Bus · Event Grid · Event Hubs
- SCIM 2.0 · SAML · OpenID Connect · OAuth 2.0
- Microservices integration patterns
- Hybrid and legacy integration bridges
Platforms
- Azure Data platform (Data Lake · Synapse · Databricks)
- Cosmos DB · SQL · PostgreSQL · Redis
- Azure AI Search & vector databases
- Enterprise analytics & BI
- Streaming analytics & CEP
- AI / RAG information retrieval
- Data mesh & federated data domains
Data architecture principles
Data is a product.
Every data asset has an owner, a documented consumer contract, a quality SLA and observable lineage. Data teams are consumer-facing, not internal-only.
Semantics before syntax.
Shared meaning across systems matters more than shared schemas. Define ontology and business terms first; formats follow.
Federated by default.
Centralised monoliths do not scale organisationally. Data domains own their data and expose it via contracts to a central discovery/governance layer.
Trust through evidence.
Lineage, quality metrics and access audit are exposed alongside the data itself so consumers can assess fitness for their purpose.
Privacy is a design property.
Consent, minimisation, purpose limitation, retention and sovereignty are architectural inputs — not compliance overlays applied afterwards.
Integration architecture patterns
Contract-first API
OpenAPI / AsyncAPI specifications live in a shared repository, versioned, generated into client stubs, published to a developer portal. Consumers can build against the contract before the implementation ships.
ARCHITECTED
Event-first workflows
Business events published to a stream; multiple consumers subscribe independently. Replay, dead-letter and idempotency are architectural requirements, not afterthoughts.
ARCHITECTED
Strangler façade for legacy
New capability delivered via API in front of legacy estate; consumers migrate gradually; legacy is decommissioned one flow at a time.
DELIVERED
Identity as a first-class integration axis
Every API call carries identity context (user, service, tenant) end-to-end. Authorisation policy is centralised; enforcement is close to the data.
ARCHITECTED
Data architecture at scale — evidence
Standards & frameworks I work with
Data standards
- ISO 8000 (Data Quality)
- ISO 11179 (Metadata Registry)
- DCAT (Data Catalog Vocabulary)
- Data Mesh principles
Integration standards
- OpenAPI 3.x · AsyncAPI · gRPC
- SCIM 2.0 · SAML 2.0 · OIDC · OAuth 2.0
- CloudEvents · JSON Schema
- REST maturity model
Governance
- DAMA-DMBOK 2
- UK Government Data Standards Authority
- Digital Scotland data principles
- GDPR · DPIA · privacy engineering
Sector-specific familiarity
- Financial: ISO 20022, SWIFT MT/MX, FpML
- Health: HL7 v2/v3, FHIR, SNOMED CT, OpenEHR
- Public sector: interoperability standards, common components