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Data Engineering

Reliable, tested data pipelines that analytics and AI can depend on.

Most of the effort in any data or AI initiative is engineering: getting data out of source systems, cleaning, joining, testing and delivering it on time. Done well, it is invisible. Done badly, every dashboard and model inherits the problems.

We build pipelines as software: version-controlled, tested, observable and documented, delivering reusable data products rather than one-off extracts.

What is included

Service offerings

04.1

Ingestion and integration

Batch, change data capture and API ingestion from enterprise systems.

  • Source connectors
  • CDC pipelines
  • Integration patterns

04.2

Streaming and real-time

Event pipelines for operational decisions and real-time analytics.

  • Event architecture
  • Streaming pipelines
  • Latency monitoring

04.3

Transformation and modelling

Tested, documented transformations into curated, reusable models.

  • Transformation layer
  • Data tests
  • Documentation

04.4

Data products

Domain datasets with owners, contracts and service levels.

  • Data product specs
  • Data contracts
  • Published products

04.5

Migration and modernisation

Move from legacy ETL and warehouses with reconciliation.

  • Migration plan
  • Converted pipelines
  • Reconciliation reports

04.6

DataOps

CI/CD, orchestration and observability for data.

  • CI/CD pipelines
  • Orchestration
  • Data observability

When to call us

Signs you need this

  • Reports arrive late or break after source changes.
  • Pipelines are undocumented and depend on one person.
  • A legacy ETL tool or warehouse is being retired.
  • Real-time data is needed but only nightly batches exist.

Approach

How we work

  1. 01Profile

    Understand sources, volumes, quality and service levels needed.

  2. 02Build

    Engineer pipelines with tests, version control and documentation.

  3. 03Publish

    Deliver data products with owners and contracts.

  4. 04Operate

    Monitor freshness, quality and cost, and fix issues at source.

Standards and frameworks

Aligned with recognised practice

  • DataOps practices
  • Open table formats (Apache Iceberg, Delta Lake)

Solutions

Where this service is applied

Data foundations

Modern Data Platforms

One governed platform for analytics and AI, designed for your scale and your region.

View solution Decision intelligence

Decision Intelligence Platforms

Connect the data, the analysis and the action behind your most important decisions.

View solution Decision intelligence

AI-Powered Reporting

Reports that explain themselves, built on numbers everyone trusts.

View solution Decision intelligence

Predictive Analytics

Forecasts and risk scores that are trusted enough to act on.

View solution Applied AI

Knowledge and Retrieval Systems

Make the organisation's knowledge findable, citable and usable by AI.

View solution

Technology

Platforms we work with

Vendor-neutral: examples of platforms commonly used in this service. Names identify technologies only and do not imply a partnership.

  • Apache Spark
  • Apache Kafka
  • Apache Flink
  • dbt
  • Apache Airflow
  • Fivetran
  • Databricks Lakeflow
  • Azure Data Factory
See the full technology ecosystem →

Insights

Further reading

Unifying a Fragmented Data Estate

A new platform does not unify a fragmented estate. Unification comes from choosing the right integration pattern for each data flow, agreeing a few shared definitions, and retiring point-to-point connections in a deliberate order.

Read →

Databricks for Pipelines, ML and LLMs

Databricks' real strength is that pipelines, models, agents and their traces share one governance layer. The price is that its newest AI features are serverless, billed on separate lines and unevenly available by region. Adopt it workload by workload, not wholesale.

Read →

Open Data Formats in Depth

The open table formats have converged on the same features. What now decides whether your data stays open is which features you switch on, which engines can still read them, and which catalog protocol brokers access.

Read →

Real-Time and Event-Driven Decisions

Real time is a property of the decision, not the data. Pay for low latency only where the decision window demands it, and when you do, put most of the engineering into correctness at the point of action, because pipeline guarantees stop there.

Read →

Talk to us about Data Engineering

Engage this service on its own or as part of a solution. We will agree scope, outcomes and the right team with you.

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Analytics and Business Intelligence

Fewer, better reports, on numbers everyone agrees on.

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