The organisation operated across 15 countries with over 50 disparate data sources, including legacy mainframe systems, multiple CRMs, trading platforms, and regulatory reporting systems. Data was scattered across on-premises infrastructure and early cloud implementations, creating significant challenges.

Key Challenges:

• ●Regulatory Compliance - Different jurisdictions required varying data residency and processing requirements
• ●Data Quality Issues - Inconsistent data formats and definitions across systems led to 40% of analyst time being spent on data preparation
• ●Operational Risk - Manual data processes created delays and increased the risk of errors in critical financial reporting
• ●Scalability Constraints - Existing infrastructure couldn't handle the growing volume of real-time trading and customer data

At the core of the architecture was Snowflake, chosen for its decoupled storage and compute capabilities, native support for multi-cloud deployments, and built-in governance features. This provided the foundation for a data lakehouse architecture that could scale elastically while maintaining strict security and compliance standards.

Open source technologies were leveraged heavily to reduce vendor lock-in, enable modularity, and accelerate delivery. Airbyte was implemented to handle extraction from over 50 source systems, enabling hundreds of data pipelines to be configured declaratively with minimal overhead. Debezium handled change data capture, allowing for near real-time replication of transactional databases into the platform without intrusive batch loads that previously disrupted business operations.

Apache Airflow served as the orchestration layer, providing a clear and auditable pipeline execution framework that enabled a distributed team of engineers and analysts to collaborate effectively. The platform processed over 2TB of data daily with 99.9% uptime.

dbt was adopted as the semantic and transformation engine, enabling both analysts and engineers to define business logic in a controlled, testable, and version-controlled manner. This democratised data transformation capabilities while maintaining enterprise-grade governance.

Key Achievements:

• ●600+ dbt models were created to transform raw data into business-ready insights
• ●Automated testing ensured data quality with 200+ data quality checks running daily
• ●Version control enabled rollback capabilities and change tracking for all transformations
• ●Documentation was automatically generated, creating a self-service data catalogue

Data governance, observability, and access control were architected from day one. The platform integrated seamlessly with enterprise identity providers using OAuth 2.0 and SAML, supported comprehensive lineage tracking through Apache Atlas, and maintained strict role-based access control across all cloud environments. Data masking and encryption ensured sensitive customer and financial data remained protected while enabling analytics use cases.

Despite the complexity of supporting three major cloud vendors (AWS, Azure, GCP) across eight different regions, the use of infrastructure-as-code (Terraform) and containerisation (Kubernetes) allowed for consistent deployments, automated testing, and minimal operational drift. The implementation followed a phased approach over 12 months:

Within twelve months, the platform was fully operational and supporting critical workloads across finance, risk management, customer analytics, and regulatory reporting. The business transformation was significant:

• ●Reporting Time Reduction: From 3-4 weeks to under 2 hours for monthly financial reports
• ●Data Quality Improvement: 99.5% data accuracy achieved through automated validation
• ●Cost Savings: £2.8M annual savings through legacy system retirement
• ●Regulatory Compliance: 100% on-time regulatory submissions across all jurisdictions
• ●Business Agility: New analytics use cases deployed in days rather than months