Cloud Data Platforms & Data Engineering

What data engineering is and why it matters

Data engineering is the discipline of building the systems that move and prepare data: the pipelines that collect it from your source systems, the warehouse or lake that stores it, and the transformations that turn raw records into clean, analysis-ready tables. It is the plumbing beneath business intelligence, analytics and AI.

It matters because everything built on top inherits its quality. A beautiful dashboard on broken data is worse than no dashboard, because it looks trustworthy while being wrong. Good data engineering is what lets you trust every number downstream, and what lets new analytics projects ship in weeks instead of stalling on missing or messy data.

• Data pipelines and integration
• Cloud warehouses and lakehouses
• ETL and ELT processing
• Data cleaning and standardisation
• Analysis-ready data models
• A trusted foundation for BI and AI

Cloud data platforms: Snowflake, Databricks, BigQuery and Fabric

We build on the leading cloud data platforms and choose the right one for your needs. Snowflake for elastic, easy-to-run warehousing, Databricks for a lakehouse that handles both analytics and machine learning at scale, BigQuery for serverless Google-native analytics, and Microsoft Fabric where you are invested in the Microsoft ecosystem.

We design the platform for your real workloads and budget, not the biggest invoice, with sensible structure, separation of environments, and the governance to keep it tidy as it grows. The result is a platform that scales with demand and stays affordable, rather than a bill that balloons as adoption rises.

• Snowflake warehouse design
• Databricks lakehouse builds
• Google BigQuery platforms
• Microsoft Fabric and Azure
• Warehouse versus lakehouse strategy
• Environment and workspace setup

Related: Cloud services (AWS, Azure, GCP)Business intelligence

ETL, ELT and pipeline development

Pipelines are the heart of a data platform: they bring data in, transform it, and keep it current. We build both batch and streaming pipelines using modern ELT patterns, where raw data lands in the warehouse and is transformed there, which is more transparent, testable and cheaper to maintain than older ETL approaches.

We use dbt to manage transformations as version-controlled, tested, documented code, so your business logic is transparent rather than hidden inside brittle scripts. Pipelines are built to recover from failure gracefully and to alert your team the moment something needs attention, rather than failing silently.

• Batch and streaming pipelines
• ELT with in-warehouse transformation
• dbt models, tests and documentation
• Incremental and real-time loads
• Orchestration and scheduling
• Failure handling and alerting

Data integration and APIs

Your data is scattered across CRMs, finance and ERP systems, databases, SaaS tools and spreadsheets, and the value is in bringing it together. We integrate these sources into your platform using managed connectors where they exist and custom API integrations where they do not, so nothing useful is left stranded in a silo.

We handle the awkward realities of integration: rate limits, schema changes, inconsistent formats and partial failures, so your platform keeps ingesting cleanly even as source systems change underneath it. The outcome is one complete picture rather than a dozen partial ones.

• CRM, ERP and finance integration
• SaaS and marketing connectors
• Custom API and webhook ingestion
• Database replication and CDC
• Schema-change resilience
• File and spreadsheet ingestion

Related: API development & integrationsWorkflow automation

Data quality, observability and governance

A platform is only as trusted as its data is correct. We build automated quality checks into the pipelines so bad data is caught before it reaches a dashboard, and data observability so you can see freshness, volume and schema at a glance and know the moment something drifts.

We add lineage so you can trace any number back to its source, and the access controls and documentation that keep a growing platform governed rather than chaotic. This is what lets you scale users and sources without the whole thing becoming a tangle nobody fully understands.

• Automated data-quality tests
• Freshness and volume monitoring
• Data lineage and cataloguing
• Schema-change detection
• Access control and governance
• Documentation and ownership

Related: Data security & compliance

Migration, modernisation and cost control

Many teams come to us with a legacy warehouse that is slow, expensive or no longer supported. We migrate you to a modern cloud platform with minimal disruption, moving data and logic carefully, running old and new in parallel where needed, and validating that the numbers match before you switch over.

We also tune platforms that have grown expensive: right-sizing compute, optimising queries and storage, and removing the waste that accumulates as usage grows. Cloud data costs can creep quietly, and a well-engineered platform is one you can afford to run at scale.

• Legacy warehouse migration
• Parallel running and validation
• Query and storage optimisation
• Compute right-sizing
• Cost monitoring and control
• Platform modernisation