Services
Data Architecture & Engineering
The foundation that makes everything else possible.
Data Architecture & Engineering
Foundations that hold up as you grow
We design, build, and manage modern data infrastructures tailored to your business. From integrating diverse data sources to transforming and preparing datasets for presentation and analysis, we take fragmented systems and unify them into something your business can actually report on and trust.
Our approach is grounded in best-practice architecture patterns, including Medallion Architecture (Bronze, Silver and Gold), that ensure data quality improves at every stage, from raw ingestion through to trusted, business-ready datasets. As a specialist provider of data engineering consulting services, we work with businesses of all sizes.
Bronze
Raw data
Landed as it arrives from your sources, untouched.
Silver
Cleaned and harmonised
Duplicates removed, formats and keys made consistent.
Gold
Curated and aggregated
Shaped for reporting, analysis and AI.
Semantic model
Meaning defined once
Core measures used by every report and AI tool.
What this looks like in practice
- Combine your data into a data lake or warehouse
- We build the centralised data environment your organisation needs, whether that’s a Lakehouse on Microsoft Fabric, a Databricks-based data lake, or a traditional warehouse.
- Clean, harmonise, transform and aggregate
- Raw data is messy. We build robust ETL and ELT pipelines that clean, deduplicate, and transform it into something reliable and consistent.
- Define semantic models with core measures
- We create a shared language for your data. Terms like “customer” or “revenue” are defined once and applied everywhere, so departments stop reporting different numbers for the same thing.
In numbers
14 → 1
systems unified in one data platform
From: Retail insight transformed~1,000
single-tenant databases harmonised
From: Building embedded AI analytics at scale10
data flows delivered in 2 months
From: From fragmented data to executive clarity
Semantic modelling and ontology
A unified data platform doesn’t just store data. It connects, contextualises, and prepares it so that ML models and AI systems can learn from truth, not noise.
- Shared meaning across systems
- A data platform is only trustworthy if every system agrees on what the numbers mean. Shared definitions replace the brittle point-to-point translations that plague traditional architectures.
- Machine-readable intelligence
- Ontologies enable automated reasoning, allowing systems to infer relationships and facts that were never explicitly stated, turning raw data into actionable knowledge.
- Built to grow with you
- Because meaning is modelled explicitly and independently of any one system, the platform can absorb new data sources, domains and use cases without rebuilding from scratch.
Frequently asked questions
What is data architecture and engineering?
Data architecture services and data engineering services help businesses design, build and manage the systems that organise their data. This includes creating data lakes, warehouses, Lakehouse environments, pipelines and models that make information cleaner, more reliable and easier to use for reporting, analytics, business intelligence and AI.
Why does a business need a modern data architecture?
A modern data architecture gives a business a structured and scalable foundation for using its data properly. Without it, data can become fragmented, duplicated or difficult to trust. A strong architecture helps teams connect sources, improve data quality and create consistent datasets for reporting, dashboards and advanced analysis.
How do ETL and ELT pipelines improve data quality?
This helps businesses turn messy source data into trusted datasets that can be used for accurate reporting, semantic models, dashboards and further analysis.
What is the benefit of using a data warehouse, data lake or Lakehouse?
A data warehouse, data lake or Lakehouse gives organisations a central place to store and manage business data. This makes it easier to combine information from different sources, reduce reporting silos and create reliable datasets for business intelligence, analytics and machine learning use cases.
How do semantic models help businesses trust their data?
Semantic models help businesses create shared definitions for important measures such as revenue, customers, utilisation or performance. This means teams can work from the same meaning instead of interpreting data differently across systems. Clear semantic modelling improves consistency, reporting accuracy and confidence in business decisions.
Other services
Data & Application Integration
Ingest your data from any source into a single location. Sync master data between your cloud applications.
Read more
Interactive Dashboards & Reporting
Provide a complete picture of your business performance. Turn data into insights with interactive Power BI content.
Read more
AI-Driven Insight & Automation
Ask your data questions in plain English, with AI summaries and trends. Governed agents on Microsoft Foundry act in your tools.
Read more
Related reading
Want to see what a well-architected data platform could look like for your organisation?
Let’s have a conversation. We’ll start by understanding where your data is today.
