Data & intelligence

Data engineering

Pipelines, orchestration, warehouse design and data contracts.

Capability in business terms

Why this capability matters

Data teams are usually judged on dashboards and defeated by pipelines. Reliability is an ownership problem before it is a tooling problem.

What we typically scope

Pipelines, orchestration, warehouse design, data contracts, quality instrumentation, cost-aware modelling.

How we approach the work

Build around freshness, recoverability and the decisions the data supports. Name an owner for every dataset.

Work we take on

Example engagement — illustrative. Typical scopes of work, not named client case studies.

The pipeline that fails silently

Why it happens
A nightly load has been partially failing for weeks; downstream reports looked plausible so nobody noticed.
How we work it
Add row-count and freshness assertions at each stage, make loads atomic, fail the run rather than the record, alert on staleness rather than on job status.
What you end up with
Pipelines that stop instead of lying, with freshness SLAs published per dataset and an owner named for each.

Warehouse spend growing faster than usage

Why it happens
Warehouse costs have tripled in a year while query volume has roughly doubled.
How we work it
Attribute cost by query, user and model; find full-table scans and unpartitioned joins; identify unused tables and abandoned dashboards; restructure the heaviest models; set warehouse sizing and auto-suspend policy.
What you end up with
Spend decomposed by owner, a ranked list of remediations with effort and saving estimates, and a monthly cost review with an accountable owner.

Two teams, two versions of revenue

Why it happens
Finance and Sales report different revenue numbers from the same warehouse and both can defend theirs.
How we work it
Trace both lineages to source, document where the definitions diverge, convene the owners to agree a single definition, implement it as a certified model, deprecate the alternatives with a migration window.
What you end up with
A certified metric layer, a written definition with an owner, and the old paths removed rather than left running.

Onboarding a data source nobody controls

Why it happens
A critical feed arrives from a partner as an unversioned CSV that changes shape without warning.
How we work it
Write an explicit schema contract, validate on arrival, quarantine non-conforming files instead of failing the pipeline, alert the partner relationship owner, version the schema.
What you end up with
A published data contract, a quarantine path, and breakages that become a conversation rather than an outage.

Standing up a platform from nothing

Why it happens
Reporting runs on spreadsheets and direct production queries; the business has outgrown both.
How we work it
Identify the ten decisions the business actually makes on data, model backwards from those, choose ingestion and warehouse to match volume rather than ambition, establish orchestration, testing and documentation from day one.
What you end up with
A working platform serving the ten decisions, with runbooks and a handover your team can operate without us.

Related business solutions

In your sector

Ways to engage

How we approach the work

Understand the estate

Map dependencies, constraints, decision owners and the current operating model.

Make the decision explicit

Record options, risks, costs and acceptance criteria before implementation.

Make the handover usable

Document changes, operating instructions and the remaining risks.

Delivery is subject to specialist fit and availability.

Start with clarity.

What would make your business easier to run?

Tell us the operational challenge. We will discuss whether a short conversation, a scoped assessment or a delivery engagement is the right next step.