Data Engineering & Analytics
One number, one meaning.
We build the pipelines, models and governance that let your board, your product team and your finance team quote the same figure and be right.
- dbt + warehouse native
- Contract-tested pipelines
- Lineage end to end
What it is
Most data problems are definition problems
When two dashboards disagree, the cause is almost never the pipeline. It is that 'active customer' was defined twice, by two teams, six months apart.
We fix the definitions first and the plumbing second: a modelled warehouse with a semantic layer, tested transformations, and lineage that answers 'where did this number come from' in one click.
- Semantic layer as the single definition
- Tested, version-controlled transformations
- Column-level lineage
- Governance that does not block analysts
Less time in reconciliation
Reported by finance teams post-migration
Pipeline reliability
Median freshness SLO attainment
To onboard a new source
Down from multi-week integration projects
Capabilities
The full path from source to decision
Ingestion
Reliable, monitored extraction from operational systems, SaaS platforms and event streams, with schema-change alerting.
- Batch + streaming
- CDC from operational stores
- Schema drift alerts
Warehouse modelling
Dimensional models that analysts can navigate without a map, built in dbt and reviewed like application code.
- Dimensional design
- dbt transformations
- Test coverage on models
Semantic layer
Metrics defined once and consumed everywhere — BI tools, notebooks and product surfaces alike.
- Metric definitions
- Governed access
- API for product use
Analytics enablement
Dashboards worth keeping, plus the training that stops your team rebuilding them privately in spreadsheets.
- Executive dashboards
- Self-serve exploration
- Analyst enablement
Data quality
Freshness, volume and distribution tests wired into the pipeline, with owners and escalation paths.
- Automated testing
- Data contracts
- Incident ownership
Governance
Classification, access control and retention that satisfy your regulator without making analysts file tickets.
- PII classification
- Row-level access
- Retention policy
Process
How we build it
01
Audit
We trace your three most-argued-about metrics from dashboard back to source and show you where they diverge.
- Lineage map
- Definition conflicts
02
Model
Core entities modelled and tested, with the semantic layer standing up the agreed definitions.
- Warehouse models
- Metric layer
03
Automate
Ingestion, orchestration and quality checks running on a schedule with alerting to a named owner.
- Orchestrated pipelines
- Quality suite
04
Enable
Dashboards, documentation and training so your analysts extend the platform without us.
- Dashboards
- Analyst training
Stack
Technologies we use here
Sectors
Industries we deliver this in
Proof
Related work
Also relevant
Related services
FAQ
Questions we are asked
Probably not yet. Most companies under a few terabytes get further with a well-modelled warehouse than with a lakehouse they cannot staff. We will tell you when the threshold is genuinely crossed.
Start with the metric you argue about most
We will trace it end to end and show you exactly where the definitions split.