Data Engineering & Analytics
One number. One definition. One place to look.
The symptom is familiar: three departments bring three different revenue figures to the same meeting, and the meeting becomes about the numbers instead of the decision. The cause is almost never the dashboard tool. It is that the definitions live in people’s heads and in the WHERE clauses of forty different queries. We fix the layer underneath.
- Governed definition per metric
- 1Governed definition per metric
- Freshness target for operational data
- MinutesFreshness target for operational data
- Insight delivered where the work happens
- In-flowInsight delivered where the work happens
Sounds like
You might recognise one of these.
Finance and ops report different numbers for the same month.
Our reports run overnight and are wrong by morning.
Every question takes an analyst three days.
We bought a BI tool and it changed nothing.
What this includes
The work, specifically.
Not every engagement needs all of it. This is the range we cover and what each part is actually for.
Ingestion and change data capture
Reliable, incremental extraction from operational databases, SaaS APIs, flat files and EDI feeds, with schema drift detection and replay after failure.
Warehouse and lakehouse modeling
Dimensional models built for the questions the business actually asks, with tested transformations, documented lineage and enforced data contracts.
Semantic layer
Metrics defined once, in version control, reviewed like code, and consumed identically by the BI tool, the API and the spreadsheet. This is what ends the three-numbers meeting.
Data quality and observability
Freshness, volume, distribution and referential tests running on every load, with alerts routed to an owner rather than a shared inbox.
Operational analytics
Getting insight back into the systems where work happens (a reorder point in the purchasing screen, a risk flag in the queue), not just onto a dashboard someone opens on Monday.
What you get
Deliverables, not documents.
- Versioned pipelines with automated tests and lineage
- Warehouse schema with a documented data dictionary
- Semantic metric layer defined in code
- Data quality monitors with named owners
- Dashboards for the decisions that matter, and no others
- Cost and freshness SLAs per pipeline
Shapes
How this usually runs.
Data assessment
2–3 weeksSource inventory, definition conflicts, quality baseline and a prioritized plan, including which reports to delete.
Warehouse foundation
8–14 weeksIngestion, core models, the first set of governed metrics and the quality framework around them.
Analytics enablement
OngoingNew domains, self-service enablement and embedded analytics inside your own products.
Tooling
What we build it with.
No tool here was picked because it was new. Where we do reach for something novel, it is in one place, for a stated reason, and it is written down.
- Warehouses
- Transformation
- Ingestion
- Consumption
Questions
Data & analytics, honestly.
If reporting queries are slowing your transactional system, or answers require joining more than one source, you need somewhere else to do the work. Below that threshold a read replica and good indexes are cheaper and we’ll say so.
Yes. The tool is rarely the problem. We’ve delivered against Power BI, Looker, Tableau, Metabase and plain SQL, and the semantic layer means the choice stops being permanent.
Classification at ingest, column-level masking, role-based access enforced in the warehouse, and region-pinned storage where regulation requires it. Access is reviewable and logged.
Further reading
What we think about this, at length.
- Data7 min read
In applied machine learning, the model is the tiebreaker
We built the full pipeline from raw network capture to a live detector, measured where the time went, and the answer was not the modeling.
- Data2 min read
The three-numbers meeting, and how to end it
When finance, sales and operations bring different figures for the same month, the problem is never the dashboard tool.
Sources
- 1.X12 Transaction Sets, X12
Next step
Tell us what’s breaking.
Forty-five minutes, no charge, no deck. We’ll tell you what we’d do, what it would likely cost, and whether you should be building this at all.