Analytics and AI both fail the same way: on inconsistent definitions, late loads and tables nobody owns. We build the layer underneath: ingestion, modelling, testing and lineage, so a revenue figure means the same thing in the board pack, the dashboard and the forecasting model.
We work warehouse-first and code-first: pipelines in version control, business logic as tested dbt models, infrastructure in Terraform. Migrations from legacy warehouses and reporting tools run in waves, with reconciliation at each step so finance signs off numbers before the old system is switched off.
Data Platform Engineering
We design and build lakehouse and warehouse platforms on Snowflake, Databricks or your cloud's native services. You get ingestion pipelines, orchestration, separate development and production environments, and Terraform for all of it.
Analytics & BI Modernisation
We move reporting off spreadsheets and ageing BI tools onto a governed semantic layer. Metrics are defined once, dashboards are rebuilt around decisions people actually make, and old reports are retired against a tracked inventory.
Data Governance & Quality
We put data contracts, automated quality tests and lineage on the pipelines that matter most. Each critical table gets an owner, freshness and accuracy checks, and an alert route when something breaks.
Streaming & Real-Time Data
Where hours-old data is too late, we build event pipelines on Kafka and Spark Structured Streaming. Fraud signals, stock positions and operational dashboards update in minutes, with replay and back-fill built in.
Legacy Warehouse Migration
We migrate on-premise warehouses and ETL estates in planned waves. Each wave includes code conversion, parallel runs and row-level reconciliation, so reports match before the legacy jobs are switched off.
Howanengagementruns
A typical Data Engineering & Analytics engagement runs about 24 weeks from kick-off to a working, handed-over system.
1
Source and report inventory
We map source systems, pipelines, reports and their owners, rank them by business use, and agree the target data model and migration waves.
2
Platform foundation
We stand up the platform in Terraform with environments, access controls, orchestration and CI for dbt, so the first pipelines land on production-grade ground.
3
Pipeline and model waves
Wave by wave we ingest sources, build tested dbt models and the semantic layer, and reconcile outputs against the legacy reports finance and operations rely on.
4
Cutover and adoption
We run old and new side by side, switch reports over, decommission legacy jobs, and train analysts on the semantic layer and quality alerts.
