Solution 01 / Data Engineering & Platforms

The foundation every AI initiative quietly depends on.

Design and build data platforms that stay correct under load, under change, and under cost pressure, so the analytics and AI built on top of them can be trusted.

Discuss Data Engineering ↗

01The problem

Most data platforms fail slowly rather than loudly. A pipeline succeeds while emitting wrong numbers; a schema change lands upstream and nothing alerts; a backfill silently double-counts. By the time anyone notices, trust in the data is gone, and the organisation quietly reverts to spreadsheets. The technical failure is rarely exotic. It is almost always missing idempotency, missing contracts, or missing observability.

Typical symptoms

Dashboards that two teams read differently, with no agreed definition to settle it
Pipelines that "succeed" while producing wrong or partial data
An upstream schema change that breaks reporting days later, discovered by a human
Backfills nobody will run without supervision, because reruns double-count
Warehouse spend rising faster than data volume or query load
A growing set of manual reconciliation steps that only one person understands

Decisions we help you make

Batch, incremental, or streaming: chosen per dataset, judged by the decision latency it actually serves
Where transformation logic lives: ELT in the warehouse, in-flight, or at the application boundary
What a data contract commits to, and what happens on the producer side when it is broken
Which datasets get freshness SLAs, and what the alert actually pages someone for
Storage and compute layout: partitioning, clustering, file sizing, tiering
Build versus buy for ingestion, orchestration, and catalog

02How we work on it

Methods we apply.

Medallion / layered modelling (raw → conformed → serving) with explicit ownership per layer

Idempotent, replayable pipeline design: deterministic keys, merge semantics, safe re-runs

Change data capture with exactly-once or effectively-once delivery semantics

Data contracts enforced in CI, with schema evolution rules and producer-side tests

dbt-style transformation testing: uniqueness, referential integrity, accepted values, freshness

Column-level lineage and impact analysis before any breaking change ships

Cost engineering: partition pruning, incremental materialisation, warehouse right-sizing, spot compute, storage tiering

Correctness is enforced between layers, not audited after the fact. A broken contract stops the pipeline at the boundary instead of surfacing three dashboards later.

03What moves

Metrics this work is measured on.

Data freshness vs. SLAPipeline success and reprocessing rateTest coverage on critical modelsTime to detect a data incidentCost per TB processedWarehouse spend vs. query volume

04What we need to start

Current warehouse or lakehouse, and how it is loaded today
Orchestration setup: DAG definitions, schedules, failure history
A list of the reports and models people actually make decisions with
Recent cloud or warehouse billing detail, at the resource level
Known pain: the datasets people distrust, and why

05Engagement path

How this becomes an engagement.

Platform Assessment

2–3 weeks

Map pipelines, quantify reliability and cost, and produce a ranked list of what to fix in what order.

Platform Build or Remediation

2–5 months

Implement the architecture, contracts, tests and observability alongside your team, not in isolation.

Operational Handover

Ongoing

Runbooks, documentation, and enough shared context that your team owns it without us.

Want to see what this looks like against your own systems?

Most engagements start with a short, fixed-scope assessment, enough to quantify the opportunity before anyone commits to a build.

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