Data Engineering & Warehousing
Most organisations do not lack data; they lack a platform the whole business trusts. Reporting runs function by function on hand-maintained loads, each team sees a different version of the numbers, and every new question becomes a new project. Data engineering replaces that with one governed platform — data marts and enterprise warehouses on ERP and operational sources, cloud warehouses where the case supports them, and big-data pipelines for high-volume, high-variety data.
We design for the volumes and latency the business actually needs (from gigabytes to tens of terabytes, near-real-time refresh where it matters), automate every load with monitoring and exception reporting, and model the data for both operational reporting and self-service. Where a cloud move is right we use native services — S3, EMR, Redshift, Snowflake — and retire proprietary ETL rather than re-licensing it.
Offerings
Enterprise data warehouse
Warehouse and data-mart design on SAP ECC, Oracle EBS, JD Edwards and other sources — modelled for reporting and self-service.
Cloud data platforms
Snowflake, Redshift and lake architectures on AWS and Azure, with the migration path from the current estate.
Automated pipelines
Scheduled, monitored load jobs replacing free-hand SQL — with error handling, recovery and exception reporting built in.
Big-data processing
High-volume, high-variety pipelines (web, social, machine data) processed with EMR/Hadoop and Python, without proprietary tooling.
Near-real-time capture
Change capture into the reporting platform in under 15 minutes where the business runs on it.
Data quality & master data
Exception reporting and master-data controls that surface and fix quality issues at the source.
Building for AI, and building with it
A reporting warehouse and an AI-ready platform are not the same thing. The second serves models and agents as well as dashboards — and it is built faster with AI in the toolchain.
- 01
AI-ready lakehouse
Bronze-silver-gold layering, open table formats, and a governed semantic layer, so the same trusted data feeds reports, feature pipelines and retrieval for LLM applications.
- 02
Data products for RAG and agents
Curated, documented, access-controlled datasets — and vector indexes over documents — packaged so an AI application can be pointed at them without a rebuild.
- 03
AI-driven data quality
Anomaly detection on volumes, distributions and freshness catches a broken feed before the business does — and before a model trains on it.
- 04
Metadata that writes itself
Column descriptions, lineage summaries and PII classification generated from the data and reviewed, so the catalogue is complete instead of aspirational.
Our approach
- 1
Architect
Agree the target platform, the data models and the latency each domain needs — with governance designed in.
- 2
Build the first domain
Deliver the most valuable (often the most complex) domain first — procurement, sales, finance — in weeks, not quarters.
- 3
Industrialise
Automate and monitor every load; migrate hand-written SQL into managed jobs; add quality and exception reporting.
- 4
Scale & hand over
Extend domain by domain, enable self-service under governance, and hand the platform to your team — or run it for you.
Outcomes
What changes
- A single view across applications, functions and processes.
- Faster delivery of information and greater trust in the data.
- Loads that run themselves — with far lower enhancement and support cost.
- A platform that supports ever-growing data and analytical needs.
Questions we're asked about data engineering & warehousing
Yes. We build warehouses and data marts on SAP ECC, Oracle EBS, JD Edwards and similar sources, pulling data without affecting operations and structuring it for reporting and analysis.
Whichever the business case supports. We design for cloud warehouses such as Snowflake and Redshift, for full analytics environments on AWS or Azure, and for modernised on-premise platforms where that is the right answer — and we are vendor-agnostic in the recommendation.
By design: exception reporting identifies quality and master-data issues in the loads themselves, so they are fixed at the source rather than discovered in a dashboard.
We deliver the most valuable domain first and aim to have it live within six to twelve weeks, with the rest of the estate following domain by domain.