Data warehouses, dashboards, and reporting that reflects your positions — built by engineers who understand data structures, calculation complexity, and the performance demands of front-to-back office reporting.
Our BI team designs and implements the reporting and DWH solutions that front, middle, and back office rely on. We build the data infrastructure behind your risk reporting, P&L dashboards, and operational processes.
End-to-end DWH design, schema modelling, and delivery. MtM, PnL, Delta, and VaR calculation pipeline integration. Scheduled runs, results extraction, and transformation layers. Batched and watermark-driven incremental loads.
Front-, middle- and back-office KPI dashboards: position reporting, P&L and reconciliation views. Custom DAX, data models and a governed semantic layer with row-level security. Performance-tuned for high-volume trade data: query folding, batched loads, aggregation tables, composite models and incremental refresh with rolling window partitioning.
Batch and real-time pipeline design and implementation. dbt-native transformation with modular, tested models. Data quality rules, observability, monitoring, and alerting. Array-based pre-calculations to avoid data expansion too early in the materialisation layers.
Oracle-to-Snowflake migrations with full data validation. Microsoft Fabric (OneLake, Lakehouse, Eventstream), Databricks for large-scale processing, and Azure Data Factory for orchestration. Array-based pre-calculations to limit early data expansion, plus custom dbt materialisation macros per layer, environment and customer.
Our client needed to consolidate granular algorithmic trading data for management reporting, but data volumes caused repeated timeouts. We developed a custom Python aggregation script that reduces the dataset from 20 million rows to approximately 2,000 — and designed a hierarchical reporting structure in Power BI for deal drill-downs and area aggregation for management consolidations.
