Data Engineering
Pipelines, warehouses, and dashboards that turn the data you already have into numbers people trust. For companies with data in six systems and no single answer to how the business is doing.
Who this is for
- Reports that take a day to assemble and are out of date when they arrive.
- Two dashboards that show different numbers for the same thing.
- A product that needs analytics features for its own customers.
- An AI initiative blocked by data nobody can get at cleanly.
What is included. Tick what you need.
Artefacts, not adjectives. Each is something you can point to at the end. Tick the ones your project needs and send the list with your enquiry; we reply with a written scope.
Technologies we use for this
The relevant slice of our technology matrix. Nothing here that we cannot staff today.
- Python (Django, FastAPI, Flask)
- Node.js (Express, NestJS)
- PHP (Laravel)
- Go
- REST
- GraphQL
- gRPC
- WebSockets
- Celery
- Redis Queue
How we deliver it
The five steps every engagement goes through, in the form they take for this service.
Scope
A call with an engineer, then a written scope: what is in, what is out, and what it costs.
Architecture
Data model, API contract, and infrastructure plan, approved before code is written.
Build
Sources connected one at a time, each validated against the numbers people already trust before the next is added.
Harden
Tests on the paths that matter, error tracking, a performance pass, and a security review.
Launch and hand over
Production deployment, monitoring, documentation, and every repository transferred to you.
From the blog
- How to build an AI analytics assistant'Ask your data a question' is a text-to-SQL problem wearing a chat interface. The architecture that makes it trustworthy: schema grounding, guardrails, and a query the user can check.
- Enterprise RAG: what changes with real documents, permissions, and scaleA RAG demo answers questions about a clean folder of PDFs. An enterprise deployment has to handle permissions per document, millions of pages, and daily churn.
- RAG architecture: the pipeline from document to answerIngestion, chunking, embedding, retrieval, reranking, generation: the six stages of a production RAG pipeline, and where each one quietly determines answer quality.
Questions we get asked
Which warehouse should we use?
PostgreSQL for most companies until it stops being enough, ClickHouse for analytics at scale, or the platform you already pay for. We recommend one after seeing volumes and queries.
How do you make the numbers agree?
One definition per metric, written down, implemented once in the transformation layer, and tested. Dashboards read from that, not from raw tables.
Can this feed AI features?
Yes. Clean, documented data is the prerequisite for retrieval and training, and the same pipelines serve both reporting and models.
What about real-time?
Most reporting is fine hourly or daily. Where seconds matter, such as fraud checks or live operations, we add event streaming for those flows only.
Who maintains the pipelines?
Your team, with tests, alerts, and documentation, or a retainer with us. dbt and Airflow are chosen partly because engineers can hire for them.
Tell us what you are building.
We reply within one business day with how we would build it, what it would cost, and which engagement model fits.
- 01Tell us what you are building
A short form or an email. No deck required, and "not sure yet" is a fine answer.
- 02A call with an engineer
Within one business day. Technical questions get technical answers, from the person who would build it.
- 03A written scope and quote
Fixed price where the scope is defined. The document is yours whether or not you go ahead.