DATA ENGINEERING
BUILDING THE DATA PLATFORMS
THAT POWER FINANCIAL SERVICES
Your analytics and risk teams can only move as fast as the platform underneath them allows. If they're waiting on manual extracts, fighting data quality issues or working around a legacy warehouse that hasn't scaled in years — the problem isn't analytics, it's engineering.
We design, build, and operationalise data platforms across Azure, AWS and on-premise environments — production-grade from day one, not proofs of concept that never make it past the sandbox.
WHAT WE BUILD
Cloud data platforms and lakehouse architectures
We design and implement cloud-native data platforms on Azure (Synapse, Fabric, Databricks) and AWS — migrating legacy warehouses, consolidating fragmented sources and building lakehouse architectures that combine the flexibility of data lakes with the governance of structured warehousing.
Data Pipelines & Orchestration
We build automated, testable data pipelines that move data reliably from source systems into analytics-ready layers. Our work covers batch and real-time ingestion, transformation frameworks, scheduling, monitoring, and error handling — using tools like Azure Data Factory, Databricks, Spark, and Python.
Data Quality & Master Data Management
Bad data breaks everything downstream. We implement data quality frameworks, validation rules, and master data management processes that give analytics and risk teams confidence in what they're working with.
Database Optimisation & Migration
We untangle inherited database systems — optimising performance, restructuring schemas, and migrating workloads to modern platforms. Our work ranges from SQL Server and Oracle tuning to full cloud migrations.