Leo

Senior Data Architect with 20+ years of experience across Insurance, Banking, Investment Management and Payments, including 6+ years leading enterprise data architecture in regulated environments. Expertise in defining target data architectures and designing scalable, secure and resilient data solutions across operational and analytical environments.

Extensive

London insurance-market experience across policy, claims, underwriting, pricing, exposure, risk and regulatory reporting, including Solvency II and GDPR. Strong expertise in conceptual, logical and physical data modelling, enterprise data models, canonical models, Data Vault and dimensional modelling. Experienced in translating business and technology requirements into practical data architecture designs and establishing standards for data ingestion, transformation, storage, integration, lineage, metadata, data quality and consumption.

Hands-on experience designing modern cloud data platforms using Azure, AWS and GCP, including data lakes, data warehouses, event-driven/streaming architectures and data products. Strong relational and NoSQL experience including MongoDB-compatible/document database technologies, Cosmos DB, DynamoDB, MongoDB and graph databases. Proven architectural leadership across underwriting, claims, actuarial, risk, finance and technology stakeholders, with strong experience aligning data solutions to enterprise architecture, security, privacy, regulatory, governance and data-quality standards.

Enterprise Data Architect at Aviva [PWC] (2026-01 – Present)

The Health Evolution Programme is Aviva's enterprise-wide strategic transformation designed to replace ageing legacy infrastructure with a digital-first, AI-ready architecture. The programme focuses on decommissioning mainframe-based systems (GMS/PAS, etc) and migrating to a modular, cloud-native ecosystem centred on Salesforce Health Cloud. By unifying data across the health estate, the programme enables advanced capabilities such as Generative AI underwriting, automated claims processing, and data mesh principle-based patterns for operational, reference and master data consumption through foundational, aggregated and consumer aligned data products per domain.

• Assessed the "as-is" data estate and defined templates for data lineage, business glossaries, data dictionaries, and data flow diagrams.
• Defined data product metadata template (includes data contract), data modelling artefact templates, rules, standards, etc.
• Created HLDs, Key Design Decisions and Initial design options across number of use cases in compliance with organization strategy, complying with existing approved patterns and securing all relevant approvals including TDA
• Reverse engineered as-is data architecture, impact of all initiatives on the enterprise data architecture and defined transition states aligning to all roadmaps.
• Designed automated pipelines for data ingestion between the Data Lake, Salesforce, and Snowflake.
• Helped creating initial version of canonical data model which would be used for Data Abstraction layer
• Defined EDM, conceptual, canonical, and logical data models to unify the health data architecture.