Numbers your team stops arguing about
Event-driven pipelines, warehouses and reporting built so the data arrives complete, on time and reconciled — engineered by people who have fixed silent data loss in production at airline scale.
How we approach it
The expensive version of a data problem is not a missing dashboard. It is two reports that disagree, a nightly job that fails quietly, and a team that has stopped believing the numbers and gone back to exporting spreadsheets. Decisions then get made on instinct while you pay for a platform nobody trusts.
We build the boring middle layer that fixes it: ingestion that handles retries and late-arriving records without dropping them, transformations that are versioned and tested, a warehouse modelled around the questions your business actually asks, and monitoring that alerts when a feed goes quiet instead of discovering it a week later in a board pack.
That includes the unfashionable parts. Idempotent writes so a replay does not double-count revenue. Dead-letter queues so a malformed record parks itself instead of killing the run. Reconciliation checks between source and warehouse so someone can answer "is this number right?" with evidence. On one airline data pipeline the headline outcomes were exactly these: silent data loss stopped, cost scaled with load rather than sitting flat, and downstream teams got numbers they could rely on.
Where it makes sense we plug the warehouse straight into the reporting or AI layer your team already uses, rather than selling you a new tool to learn. Projects are scoped and fixed-price after a free 30-minute call, and everything — pipeline code, models, infrastructure — belongs to you.
Data Engineering, done properly
Event-driven pipelines
Ingestion built on queues, streams and Lambda that absorbs bursts, retries safely and scales cost with volume instead of running a warm cluster all night.
Warehouse modelling
A data model shaped around the questions you actually ask, with versioned transformations and tests — so a metric means one thing across every report.
Data quality and reconciliation
Automated checks between source systems and the warehouse, with alerts on volume anomalies, schema drift and feeds that have gone silent.
Reporting and dashboards
Dashboards for the handful of numbers that drive decisions, wired to the warehouse so they refresh themselves and nobody maintains a spreadsheet by hand.
System consolidation
Pulling CRM, finance, product and operational data into one place, so cross-system questions stop requiring three exports and a VLOOKUP.
AI-ready foundations
Clean, documented, access-controlled data is what makes retrieval and AI features work at all — we build the foundation before anything is grounded on it.
From first call to launch
- 01
Map the questions
We start with the decisions you need to make and work backwards to the data required, rather than moving everything you have and hoping it turns out useful.
- 02
Audit the sources
What exists, how reliable it is, where it currently breaks, and what it costs to move. This is where silent failures and duplicate records usually surface.
- 03
Build incrementally
One pipeline and one set of trustworthy numbers first, in production and in use, before the next source is added. Useful early beats complete eventually.
- 04
Monitor and hand over
Alerting on freshness, volume and quality, plus documentation and a walkthrough so your team can add sources and models without us.
Data Engineering questions, answered
We already have dashboards nobody trusts. Can you fix that?
Usually the dashboards are fine and the pipeline underneath is not. We trace a disputed number from source to report, find where records are being dropped, duplicated or silently transformed, and fix that first. Trust comes back when a reconciliation check can prove the figure, not when the chart gets prettier.
Do we need a warehouse, or is this overkill for our size?
Plenty of businesses do not need one, and we will say so. If two systems and a scheduled export answer your questions, keep it. A warehouse earns its place when questions span several systems, when history matters, or when people are spending hours a week assembling the same report by hand.
What does a data project cost?
A first pipeline and reporting layer typically starts around £15,000, with the scope and price fixed in writing after a free scoping call. Consolidating several sources with quality checks and modelling costs more. We phase the work so something useful is live early rather than billing for months before you see anything.
Which tools do you use?
Predominantly AWS — Kinesis or EventBridge, Lambda, Step Functions, S3, DynamoDB, Athena or Redshift, OpenSearch where search matters — with Python and TypeScript for transformation. We fit the tools you already run where that is sensible; we are not interested in a migration for its own sake.
Can you work with our existing data team?
Yes, and it is often the best value. We take the platform and reliability work — pipelines, infrastructure, testing, alerting — while your analysts keep the domain knowledge and the modelling, with us in the pull requests to transfer the engineering practices.
Related services
Cloud & Platform Engineering
AWS architecture, infrastructure as code and platforms that stay up.
Systems & API Integration
Making the systems you already run talk to each other properly.
AI Development
Production-grade AI features and products — engineered to work reliably, not just demo well.
Internal Tools & Portals
Software for your own team — dashboards, portals and admin tools.
Let's talk about your project
Tell us what you're trying to achieve. A free 30-minute chat, no obligation — we'll tell you honestly what we'd recommend, what it would cost and what it should pay back.