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Services / Data Engineering

Data Engineering

A dashboard is only as trustworthy as the pipeline underneath it. We build ingestion that handles late-arriving and duplicated records, models that are tested like software, and lineage that lets anyone answer where a number came from without booking time with a data engineer.
pipeline success rate across managed systems
99.98%
median source-to-dashboard latency
18min
average warehouse spend reduction
44%
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What you get

Deliverables

Concrete artefacts, handed over in your repositories.

01

Ingestion layer

Connectors with explicit watermarking and replay, so a failed run is re-runnable rather than a manual reconciliation exercise.

02

Tested models

Transformations with unit tests, referential integrity checks and freshness assertions running in CI before anything reaches production.

03

Semantic layer

One definition of revenue, active user and churn, enforced in code so two dashboards cannot disagree.

04

Runbook

What alerts, what it means, and what to do about it. Written for whoever is on call, not for the person who built it.

Capabilities

What this covers

  • Batch and streaming ingestion
  • Dimensional and event modelling
  • Idempotent, replayable transformations
  • Data quality tests and freshness alerting
  • Warehouse cost and performance tuning
  • Self-serve BI and semantic layers
Tooling

What we usually reach for

  • dbt
  • Snowflake
  • BigQuery
  • Airflow
  • Kafka
  • Postgres
  • Metabase

Tooling is a consequence of the problem, not a starting position. If your team already runs something that works, we use that rather than charging you for a migration you did not ask for.

Related work

Where this has been applied

Halcyon Bio / Life sciences

One warehouse for eleven instruments that disagreed about time

Vantage Freight / Logistics

Retiring the spreadsheet that ran the dispatch desk

Questions

Data Engineering, specifically

Yes. Most engagements start by instrumenting and testing what already exists rather than replacing it. A migration you cannot verify is a migration you should not run.

Every transformation is written to be idempotent and partition-addressable, so a backfill is the same code path as a nightly run, just with a wider window.

Column-level classification, masking policies and audited access are set up in the first phase, not retrofitted after a security review.

Also from us

Other services

AI Systems

LLM pipelines, retrieval, agents and the evaluation harness that keeps them honest.

Web & Product

Next.js applications, commerce and design systems built for conversion and longevity.

Automation

Workflow orchestration and systems integration that removes manual work permanently.

Next step

Tell us what you are building.

A system you want built, a model that has to survive real traffic, or a process that should have been automated a year ago. The first conversation costs nothing, and occasionally ends with us telling you not to build it.

Start a projecthello@brainfullstack.com

Reply within one working day

A person, not an autoresponder.

A 30-minute call, no deck

We ask about constraints, not budget.

A written view within a week

Including the case for not proceeding.