Data Warehousing Services
Cloud warehouse design, dimensional modelling, migration and governed access.
Data engineering moves data from where it's created to where it's useful — reliably, securely and in a shape people can understand. We design and build data platforms on modern cloud technology, from ingestion pipelines to warehouses and lakehouses, through to the reports and models that use them.
Explore Data Engineering services.
Each one can be engaged on its own or combined into one data engineering programme.
Data Lake & Lakehouse Solutions
Scalable lake and lakehouse design with medallion layers, open tables and catalogues.
ETL and ELT Development
Batch, incremental and CDC pipelines with orchestration, tests and monitoring.
Azure Data Factory Development
Metadata-driven ADF pipelines, integration runtimes, monitoring and CI/CD deployment.
Databricks Solutions
Lakehouse design, Unity Catalog, pipelines, cost control and ML on Databricks.
Apache Spark Development
PySpark and Scala batch and streaming jobs, tuned for speed and cost.
Data Integration Services
API, event and iPaaS integration that keeps ERP, CRM and operational systems in sync.
Business Intelligence Services
KPI definitions, semantic models and Power BI, Tableau or Looker dashboards.
Data Analytics Services
Exploratory, diagnostic and customer analytics turned into clear recommendations.
The problems Data Engineering solves.
Pipelines, warehouses, lakehouses, integration, BI and analytics on modern cloud platforms.
Reports take days to prepare and numbers differ between teams.
Data from key systems can't be combined.
Pipelines break silently and nobody notices until a report is wrong.
AI and analytics plans stall because the data isn't ready.
What Data Engineering covers.
Data warehousing
Structured, modelled data for reporting and analysis.
Data lakes and lakehouses
Scalable storage for raw and refined data of every type.
ETL and ELT
Reliable pipelines that extract, load and transform data.
Platform engineering
Azure Data Factory, Databricks, Spark and cloud-native services.
Data integration
Connecting applications, APIs and databases.
BI and analytics
Dashboards, semantic models and analysis built on clean data.
The layers of a modern data platform.
A data platform is a chain: if one link is unreliable, every report downstream inherits the problem. We design each layer with an owner, tests and monitoring.
The tools differ between platforms; the layers don't.
- 01
Ingestion
Batch extracts, change data capture or event streams bring data from ERP, CRM, SaaS APIs and databases into the platform, landing it unchanged.
- 02
Storage
A lake or lakehouse keeps raw and refined data in open formats such as Parquet or Delta; a warehouse holds modelled tables for reporting.
- 03
Transformation
SQL or Spark models clean, join and reshape the data, usually in bronze, silver and gold stages, version-controlled and tested.
- 04
Orchestration
A scheduler — Data Factory, Airflow or Databricks Workflows — runs jobs in dependency order and retries or alerts on failure.
- 05
Serving
Semantic models, BI tools, APIs and feature tables give people and applications the data in the shape they need.
- 06
Governance
Access control, lineage, data quality checks and a catalogue so people know what each dataset means and who owns it.
Clear, owned outputs.
- A data platform architecture
- Ingestion and transformation pipelines
- Data models and documentation
- Data quality tests and monitoring
- Dashboards and a handover to your team
Why it's worth it.
One version of the truth
Teams work from the same definitions and data.
Faster insight
Automated pipelines replace manual data preparation.
Ready for AI
Clean, governed data is the foundation for machine learning.
When Data Engineering is the right move.
- A company replacing spreadsheet-based reporting.
- Consolidating data from ERP, CRM and marketing systems.
- Modernizing an on-premises data warehouse in the cloud.
Technologies & platforms
- Azure Data Factory
- Databricks
- Apache Spark
- Snowflake
- BigQuery
- Amazon Redshift
- dbt
- Apache Airflow
- Power BI
- Microsoft Fabric
How Data Engineering work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Data Engineering questions.
Which data platform should we use?
It depends on your cloud, data volumes, skills and use cases. We compare options such as Databricks, Snowflake, BigQuery and Microsoft Fabric against your needs.
Can you work with our on-premises databases?
Yes. Hybrid pipelines can securely extract data from on-premises systems into a cloud platform.
How do you ensure data quality?
With automated tests in pipelines, reconciliation against source systems, monitoring and alerts, and clear ownership of each dataset.
Is your reporting built on data you trust?
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.