Data Engineering & Enterprise Technology
Pipelines, warehouses, dashboards and database work that make reporting consistent and fast.
What this is
Reporting problems are usually definition problems. We build the data layer underneath: tested pipelines, conformed tables, documented metric definitions and dashboards that reconcile. Plus the database work — schema design, indexing, query tuning — that keeps the application fast as data grows.
When teams call us about this
If more than one of these is familiar, there is usually a case worth scoping.
- Two reports give different answers for the same metric and both are defended.
- Reporting runs against production and slows the application during business hours.
- A query that was fine at launch now times out.
- Every new report requires a developer and a week.
What this covers
Each item below is part of this service line. Where an offering needs a specialist partner, it is marked as such rather than implied.
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Data engineering and analytics
Ingestion, transformation and modelling with tests and documented lineage.
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Business intelligence and dashboards
Dashboards built on governed definitions so figures reconcile.
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Database design and optimisation
Schema design, indexing, partitioning and query tuning.
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Enterprise system integration
Connecting ERP, CRM, finance and operational systems into one reporting layer.
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IoT and connected applications
Ingesting, storing and reporting on device and telemetry data.
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SAP and enterprise application services
Integration with SAP and comparable enterprise platforms at the data and API layer.
With a specialist partner
How we scope this: SAP and comparable enterprise platform work is scoped at the integration and data layer. Functional module configuration and licensed platform implementation are delivered with a specialist partner where your project needs them.
What you receive
Artefacts that end up in your repositories and your cloud account, not locked inside our team.
- Ingestion pipelines with validation, scheduling and failure alerting
- Modelled, conformed tables with tested transformations and documented lineage
- Agreed metric definitions implemented once and reused everywhere
- Dashboards covering the decisions your teams actually make
- Reporting workload separated from the transactional database
- Schema design, indexing and query optimisation
- Data quality checks that fail loudly rather than silently
Typical engagements
The shapes of work we are asked for most often in this area.
Single source of truth
Ends the argument about which report is correct by defining metrics once.
Reporting off production
Removes analytics load from the transactional database.
Query performance
Fixes slow pages and timeouts through indexing and query work.
Operational dashboards
Gives teams the daily numbers they currently assemble by hand.
Device and telemetry reporting
Turns connected device data into usable operational reporting.
How we run it
Discovery
Sources, metric definitions, owners, volumes and current pain points.
Modelling
Conformed tables and metric definitions agreed with the business.
Build
Pipelines, transformations, tests and scheduling with failure alerting.
Presentation
Dashboards and reports with permissions and documented definitions.
Performance
Indexing, partitioning and query tuning against real query patterns.
Handover
Documentation, lineage, runbooks and training for your analysts.
Products we built and sold
Our own commercial platforms, built with this engineering stack. These are products, not client references.
RideOn
A ride-hailing and fleet management platform, now with agentic dispatch, driver support and demand intelligence.
View product Multivendor marketplaceUniBooker
A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
View product Classifieds marketplaceClassify
A classifieds marketplace with agentic listing creation, moderation and buyer-seller matching.
View productData Engineering & Enterprise Technology — FAQs
Including the questions where the honest answer is "it depends", and what it depends on.
Do we need a data warehouse?
Not always. With a few sources and modest volume, careful modelling on a read replica is enough. Many sources, heavy transformation or long history make a warehouse the better foundation. We size it to your situation.
Why do our reports disagree?
Almost always because the metric is defined differently in each. The fix is agreeing one definition and implementing it once, which is organisational work as much as technical.
Can you speed up our existing database?
Usually substantially. Indexing, query rewriting and schema adjustments on real query patterns deliver most of the gain, well before any hardware change.
Do you work with SAP?
At the integration and data layer, yes. Functional module configuration and licensed implementation work we scope with a specialist partner rather than claim ourselves.
Services that pair with this
API Development & System Integration
Documented APIs and integration layers that keep your systems consistent without manual re-entry.
Read moreCloud, DevOps & Infrastructure
Infrastructure as code, CI/CD pipelines, containerisation and monitoring you can operate yourself.
Read moreCustom Software Development
Applications shaped around how your business actually works, not around a product roadmap.
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