Most organisations have more data than they know what to do with. The problem is rarely collection - it's structure, quality, accessibility, and trust. Data engineering is the discipline of building reliable pipelines and platforms that turn raw data into something teams can make decisions with.
What we deliver
Data platform design. We design data architectures that match your organisation's maturity and needs. Lakehouse on Databricks, warehouse on Snowflake, hybrid approaches with Azure Data Lake and SQL Server - the right choice depends on your data volumes, query patterns, team skills, and budget.
Pipeline development. We build data pipelines that extract from source systems, transform reliably, and load into target platforms. Azure Data Factory, Databricks notebooks, custom .NET services, event-driven ingestion from Service Bus or Event Hubs. We design for idempotency, error recovery, and monitoring.
Data integration. We connect systems that weren't designed to talk to each other. CRM to data warehouse, ERP to reporting platform, third-party APIs to internal data stores. We handle schema mapping, data quality checks, deduplication, and incremental sync.
SQL Server development. Query optimisation, indexing strategies, stored procedure design, database schema evolution. We work with SQL Server at scale - partitioning, columnstore indexes, Always On availability groups, and performance tuning for high-throughput OLTP workloads.
Azure data services. Cosmos DB for globally distributed low-latency workloads, Azure Search for full-text and vector search, Blob Storage and Data Lake Storage for unstructured data, Synapse for analytics. We choose services based on access patterns and consistency requirements.
Data quality and governance. Schema validation, data lineage tracking, freshness monitoring, anomaly detection. We build data quality checks into pipelines so problems are caught at ingestion, not discovered in a board report three weeks later.
How we work
We treat data platforms as products with clear consumers, SLAs, and feedback loops. Data engineers embed with analytics and product teams to understand what data they need, how fresh it needs to be, and what "correct" means in their context.
Technologies
SQL Server, Snowflake, Databricks, Azure Data Lake, Azure Data Factory, Cosmos DB, Azure Search, Event Hubs, Service Bus, Python, C#, Spark, dbt.