Services
Data Engineering & Analytics
Reliable pipelines, well-modelled warehouses and lakehouses, and analytics your teams can trust.
The problem
Most organisations already have the data they need. The trouble is that it sits in different systems, arrives late, breaks quietly and means different things to different teams.
When reports disagree, people stop trusting them and fall back on spreadsheets. Analysts spend their week fixing extracts instead of answering questions, and every new request takes longer than the last.
What we deliver
- Data platform design for warehouse, lakehouse or hybrid patterns, sized to your volumes and budget
- Ingestion and transformation pipelines with scheduling, retries, alerting and clear ownership
- Dimensional and analytical models so metrics are defined once and reused everywhere
- Data quality checks that catch problems before they reach a report
- Platform migration from legacy databases and on-premises tools to cloud services
- Documentation and runbooks your team can actually use
We work across Microsoft Azure, AWS, Microsoft Fabric, Snowflake, SQL, Python and PySpark, and choose based on what you already run and what your team can support.
How an engagement works
01
Data assessment
We review your sources, current pipelines and pain points, and produce a short findings report with priorities.
02
Target design
We agree the target architecture, data model and the first use case to deliver end to end.
03
Incremental build
We deliver one valuable data product at a time, each tested, documented and in production.
04
Handover
We train your team, hand over runbooks and agree any ongoing support.
Illustrative scenario
From nightly spreadsheets to a governed lakehouse
A regional operations team compiles a weekly performance pack by hand from four systems. It takes two days and the numbers rarely match finance’s view.
ProductDoc would map the four sources, agree shared definitions with both teams, and build automated pipelines into a lakehouse with a single modelled layer. The weekly pack becomes a refreshed dataset, and the two days go back to analysis.
This is an illustrative example of how an engagement could run, not a description of a specific client.
Frequently asked questions
Do we need to move everything to the cloud first?
No. We often start by connecting to existing systems where they are and modernise in stages, so you see value before any large migration.
Which platform should we choose?
It depends on your existing licences, skills, data volumes and security requirements. We set out the trade-offs and recommend one, rather than defaulting to a favourite.
Can you work alongside our internal team?
Yes. Many engagements are a mix of building and coaching, so your team owns the platform when we step back.
Related services
Business Intelligence & Reporting
Dashboards and reports built around real decisions, with metrics everyone agrees on.
Cloud & Modernisation
Move ageing systems to the cloud, or modernise what you run, without disrupting the business.
Application Development
Web applications and internal tools that fit how your organisation actually works.
Talk to us about data engineering and analytics
Tell us what you are trying to achieve. We will reply within one working day with a few questions or a suggested next step.