Power BI and
semantic architecture.

We design dimensional models with explicit grain. We define what each fact represents, at what level of detail, and how it aggregates. Star schema is the default because it scales. Snowflake patterns are introduced where justified by reuse, cardinality, or maintenance considerations. Conformed dimensions are treated as non negotiable for KPI consistency across subject areas.

Enterprise data is not tidy. We handle many to many complexity through bridge tables and carefully controlled relationships. We implement slowly changing dimension (SCD) strategies where historical accuracy matters. We model degenerate dimensions and transaction identifiers where required. We design the semantic layer to support multi entity reporting, consolidation, and currency translation with controlled logic rather than report by report hacks.

Measures are engineered using measure branching to prevent duplication and drift. Time intelligence is centralised where appropriate using calculation groups. We govern naming conventions, folder structures, and documentation so models remain maintainable by internal teams. We validate DAX against business rules and reconciliation checks, not just visuals.

Performance is engineered, not guessed. We evaluate Import, DirectQuery, and composite strategies based on latency needs, source capability, refresh constraints, and capacity behaviour. We optimise cardinality, reduce unnecessary columns, manage relationships for efficient filter propagation, and apply performance profiling to identify expensive measures and bottlenecks. We treat query folding and transformation discipline as part of performance, not as an afterthought.

We design row level security (RLS) frameworks that scale across regions, entities, and role structures. We avoid fragile manual role sprawl by designing access models that align to organisational structure. We document security intent, implement validation testing, and ensure that security remains consistent across reports.