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Data engineering & analytics

Turn raw data into confident decisions

We design, build and run modern data platforms — from ETL and ELT pipelines to lakehouses on Microsoft Fabric, Snowflake and Databricks, across AWS, Azure and Google Cloud.

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What we deliver

Data engineering end to end

Reliable pipelines, well-modelled data and governed platforms your teams can trust.

01

ETL Pipelines

Extract, transform and load data from your source systems into clean, analytics-ready models.

02

ELT Pipelines

Load raw data first, then transform it inside your warehouse or lakehouse using its own scalable compute.

03

Warehouse & Lakehouse

Modern architectures that bring structured and unstructured data together in one place.

04

Data Integration

Connect databases, SaaS apps, files and APIs with monitored, repeatable pipelines.

05

Data Quality & Governance

Validation, lineage and access controls so everyone can trust the numbers.

06

Analytics & BI Enablement

Semantic models and dashboards that put insight in front of decision-makers.

ETL or ELT?

The right pattern for each workload

We help you choose — and often combine — both approaches.

ETL — transform before loading

Data is cleaned and shaped in a pipeline before it lands in the target. A strong fit for strict data contracts, sensitive data that must be masked early, and legacy or on-premises targets.

Azure Data FactoryAWS GlueDataflowSSISSparkPython

ELT — load first, transform in place

Raw data is loaded quickly, then modelled inside the warehouse or lakehouse. Ideal for cloud platforms, large volumes and teams that want flexible, version-controlled transformations.

dbtSQLSnowflakeFabricDatabricksBigQueryRedshift
Platforms

Data platforms we work with

Microsoft Fabric, Snowflake and Databricks — on AWS, Azure or Google Cloud. Tap a platform to see how we help.

Microsoft FabricOneLakeData FactorySpark notebooksPower BI
Microsoft Fabric

What we do with Microsoft Fabric

Microsoft’s unified analytics platform that brings data engineering, warehousing, real-time analytics and Power BI together on OneLake.

  • OneLake & lakehouse setup
  • Data Factory pipelines & dataflows
  • Spark notebooks
  • Warehouses & SQL endpoints
  • Power BI semantic models
  • Workspace & capacity governance
Approach

How we deliver

A clear path from scattered data to a platform that runs itself.

✓Built to scale

  1. Discover & assess

    We map your sources, reporting needs and pain points, and agree what success looks like.

  2. Design the architecture

    We choose the right platform and patterns — ETL, ELT or both — and design the data model.

  3. Build & automate

    Pipelines, transformations and orchestration are built as code, with CI/CD from day one.

  4. Test, govern & document

    Data quality checks, lineage and access controls are built in, not bolted on.

  5. Run & optimise

    We monitor, support and tune performance and cost as your data grows.

Outcomes

What a modern data platform gives you

One source of truth

Consistent, well-defined metrics across every team and report.

Faster reporting

Automated pipelines replace manual spreadsheets and late reports.

Scales with you

Cloud platforms grow with your data volumes and users.

Secure & governed

Role-based access, lineage and auditing protect sensitive data.

Automated & monitored

Pipelines that alert on failures and recover cleanly.

AI-ready data

Clean, well-structured data that is ready for ML and AI use cases.

Ready to modernise your data?

Tell us about your data sources and goals — we will suggest the right platform and next steps.

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