Data Engineering

Data that arrives on time, ready to use

We design and build the data platforms and pipelines behind your reporting and AI: lakehouses, medallion layers, metadata-driven pipelines and the move off legacy warehouses. We work on the platform you have chosen, or help you choose one, and use AI-assisted tooling to speed up the build.

Capabilities

What this service covers

01

Data Platforms

Lakehouse and warehouse architecture on Fabric, Databricks, Snowflake, BigQuery or Redshift, with Bronze, Silver and Gold layers sized to your workloads and your team's skills.

What we deliver
Platform architecture documentLakehouse or warehouse buildOperations runbook
Example

Several business systems feed one platform, with each layer documented so the team knows where every table comes from.

02

Pipelines & Integration

Metadata-driven pipelines that load databases, files, APIs and document libraries, log every run and alert an owner when something fails.

What we deliver
Control tables and generic pipelinesRun logs per tableOwner alerts for failed or stale loads
Example

Adding a new source means adding a row to a control table, and a failed overnight load is reported to its owner before the morning meeting.

03

Migration & Modernization

Moving legacy warehouses and on-premises databases to a modern cloud platform in waves, rationalizing what is still used and proving the numbers match before cutover.

What we deliver
Object inventory and migration wavesRebuilt models on the new platformMeasure-by-measure reconciliation
Example

A legacy warehouse moves to the cloud one subject area at a time, and users switch only after the reconciled numbers match.

04

DataOps & Monitoring

Source control, CI/CD and monitoring for the data platform, so changes are reviewed and failures are visible.

What we deliver
Git-based workflow for pipelines and modelsAutomated deploymentsPlatform health dashboard
Example

Changes to pipelines and notebooks are reviewed and promoted from test to production instead of being edited live.

Platforms we work with
Microsoft Fabric and AzureDatabricksSnowflakeAWS (Redshift, Glue)Google BigQuerydbt and Airflow

We work on the platform you have chosen, or help you choose one.

Nexus Accelerator ยท 30 days

Reliable Pipelines

Data loads on schedule, and a failure alerts the right person in minutes.

Want results fast? This accelerator delivers one outcome in about 30 days.

What success looks like

  1. Every source in scope loads on schedule.
  2. A failed load alerts its owner within minutes.
  3. Your team adds new sources by configuration.

Discuss your Data Engineering project

Tell us which systems feed your reporting and how you find out today when a load fails.