Service
Data Engineering
We build the pipelines and data models that turn scattered operational records into something the business can report on, and something an AI system can safely reason over.
What does a data engineer build?
Data engineering builds the systems that move data from operational sources into a modelled, queryable form. That means ingestion pipelines, a warehouse or lakehouse, transformation logic that produces consistent business definitions, quality tests that catch bad data before it reaches a dashboard, and lineage so any number can be traced back to its source.
It is also the prerequisite for most AI work. Retrieval systems and agents are only as reliable as the data they read, so the pipeline and modelling layers usually have to come before the model layer in any serious AI programme.
Reporting problems are rarely dashboard problems. Two teams produce different revenue figures because each defines revenue differently, the definitions live in spreadsheets, and nobody can trace either number back to a source record.
We fix that at the modelling layer. Business definitions are written once, as tested transformations in version control. Pipelines are idempotent and reprocessable. Quality tests run before data is published. Lineage makes every figure traceable.
Business problems
What this usually solves.
The situations clients describe when they start this conversation.
Numbers that disagree between teams
The same metric is calculated differently in each report because the definition is not shared or versioned.
Reporting straight off production
Analytical queries run against the operational database, degrading application performance and constraining what can be asked.
Pipelines that break quietly
A schema change upstream stops a load, and the dashboard keeps showing yesterday’s data as if it were current.
Data not usable for AI
Records lack the structure, quality and access controls needed to ground a retrieval system or agent.
Capabilities
What we bring to it.
Ingestion pipelines
Batch and streaming ingestion from databases, APIs, files and SaaS platforms, with schema drift handling.
Warehouse architecture
Layered modelling from raw through staging to curated marts, with clear ownership at each layer.
Transformation and modelling
Business logic as tested, version-controlled SQL with documented definitions and dependency graphs.
Data quality
Freshness, uniqueness, referential and range tests that block publication when they fail.
Reporting enablement
Curated semantic layers that analysts and BI tools can query without re-deriving business logic.
AI-ready data
Document pipelines, embedding generation, chunking strategy and access controls for retrieval systems.
Deliverables
What we build
- Operational reporting warehouses for finance, sales and service data
- Customer-facing analytics embedded inside SaaS products
- Event pipelines capturing product usage for metering and insight
- Document processing pipelines producing structured, queryable records
- Embedding and retrieval pipelines that ground AI features
- Migration of spreadsheet-based reporting onto governed data models
Stack
Technology approach
We prefer well-understood SQL-first tooling. Most organisations do not need a distributed stack; they need consistent definitions and tests that run.
| Layer | What we use |
|---|---|
| Ingestion | Change data capture, API extraction, scheduled batch, event streams |
| Storage | PostgreSQL, Azure SQL, cloud object storage for raw and archival layers |
| Transform | Version-controlled SQL models with tests and dependency graphs |
| Orchestration | Scheduled and event-triggered pipelines with retry and alerting |
| Serving | Curated marts, reporting APIs, embedded analytics in product |
| AI | pgvector embeddings, chunking pipelines, per-tenant retrieval isolation |
Process
How we deliver.
- 01
Discover
Understand business requirements and existing systems.
- 02
Architect
Design product, cloud, integration and data architecture.
- 03
Engineer
Build production-grade software.
- 04
Launch
Deploy, integrate and validate.
- 05
Scale
Optimize, monitor and evolve.
Case studies
Relevant work
FAQ
Data Engineering — questions we are asked
Do we need a data warehouse?
If reporting queries are slowing your production database, if teams disagree about basic metrics, or if you need to combine data from several systems, then yes. If you have one system and simple reporting needs, a well-designed read replica may be enough for now.
How long before we see useful reporting?
Usually a first vertical slice — one source, one modelled domain, one set of tested definitions — arrives well before the full platform. Building the whole warehouse before publishing anything is how these programmes lose momentum.
Can this run on the database we already have?
Often, yes. PostgreSQL handles analytical workloads well at the volumes most mid-sized businesses operate at. Introducing a specialised warehouse is a decision driven by data volume and query patterns, not by default.
How does data engineering relate to AI projects?
Directly. Retrieval quality is bounded by data quality. If documents are unstructured, records are duplicated and access rules are unclear, an AI feature built on top will inherit all of it. The pipeline work usually has to come first.
Planning data engineering work?
Tell us about the systems involved and the constraints. We will come back with an architecture and a delivery sequence.