Expert capabilities in Data Architecture for enterprise data and analytics solutions
Data architecture provides the blueprint for how data flows through an organization, from source systems to analytical insights. It encompasses the design of data models, storage systems, integration patterns, and governance frameworks that enable scalable, reliable analytics.
This competency includes both strategic architecture planning and hands-on implementation of data platforms that align with business objectives while maintaining technical excellence and operational efficiency.
Data modeling forms the foundation of effective data architecture, providing structure and meaning to raw data assets across the enterprise.
Landing zone for ingested data in its original format, providing an immutable record of source systems with minimal transformation.
Standardized, cleaned, and validated data with consistent schemas, data types, and business rules applied for downstream consumption.
Aggregated, enriched data optimized for specific business use cases, analytics, and reporting requirements.
Scalable, auditable modeling methodology that separates business keys, descriptive attributes, and relationships into distinct structures for maximum flexibility and historical tracking.
Core Data Vault structures that separate business keys (Hubs), relationships (Links), and descriptive attributes (Satellites) for maximum agility and auditability.
Traditional dimensional modeling approaches including star and snowflake schemas optimized for analytical workloads and business intelligence.
Fact and dimension tables designed for optimal query performance, user comprehension, and consistent business metrics.
Real-time data modeling approaches that capture business events as they occur, enabling event sourcing and stream processing architectures.
Designing event schemas and stream processing patterns for real-time analytics and operational decision-making.
Designing scalable, resilient data platforms that support diverse workloads and growth requirements.
Leveraging cloud platforms for elastic scalability, managed services, and cost optimization while maintaining security and compliance requirements.
Implementing data lakes that support both structured and unstructured data with proper organization, cataloging, and access controls.
Architecting solutions that span on-premises and cloud environments, supporting data residency requirements and vendor diversification strategies.
Establishing robust, scalable patterns for data integration that support both batch and real-time requirements.
Creating standardized APIs for data access that enable self-service while maintaining governance and security standards.
Implementing real-time data synchronization patterns that minimize impact on source systems while ensuring data freshness.
Applying domain-driven design to data architecture, enabling decentralized data ownership while maintaining consistency and discoverability.