Data
Numbers your leadership team actually trusts
Almost every company we meet has dashboards. Far fewer have dashboards their executives believe. The difference is rarely the visualisation tool. It is lineage, definitions, tests and the discipline to have one place where a metric is defined.
Scope
What this covers
Pipelines, batch and streaming
Ingestion from operational databases, third party APIs and event streams, with idempotent loads, backfill support and alerting when a source goes quiet.
Warehouse modelling
Dimensional models in Snowflake, BigQuery or Postgres, built so a new question does not require a new pipeline.
Semantic layer and metric definitions
One canonical definition of revenue, active user and churn, versioned in code, so finance and product stop arguing about whose number is right.
Data quality and lineage
Tests on freshness, volume, uniqueness and referential integrity that fail the pipeline rather than quietly publishing bad numbers.
Reporting and self service
Dashboards for the questions that repeat and a governed self service layer for the ones that do not, plus training so analysts are not blocked on engineering.
Stack
What we build it with
Tools are chosen per project and justified in an architecture decision record. This is what we reach for most often in this practice.
Deliverables
What you actually receive
- Documented lineage from source column to dashboard tile
- Metric definitions reviewed and signed off by finance
- Automated data quality tests running on every load
- Backfill and replay procedure that has been rehearsed
- Analyst training and a written query cookbook
Related case studies
Where we have done this
Ingesting 2.1 million vehicle events an hour at 42 percent of the previous cost
Eleven thousand vehicles emitting telemetry every two seconds had outgrown a design that stored everything at full resolution forever. We changed what gets kept rather than buying more storage.
Rebuilding a payments ledger that was losing money it could not find
The original system tracked balances rather than transactions, so a discrepancy could be observed but never explained. We migrated to an immutable double-entry ledger with zero downtime and no change to the customer-facing product.
Questions
What clients ask first
Do we need a warehouse, or can we report from production?
Our numbers disagree between systems. Can you fix it?
Can you work with the BI tool we already bought?
Services
Practices that pair with this one
Product engineering
SaaS platforms and web products built to carry real load
Mobile engineering
iOS and Android apps that hold up outside the demo
Agentic and generative AI
AI features that pass review, not just demo well
Cloud and platform engineering
Infrastructure your team can operate at three in the morning
Technology consulting
Straight answers before you spend the budget
Need data engineering and analytics?
Send a short brief and a senior engineer will read it. You get a written response with our honest read on scope, risk and cost within one business day. No discovery call required to get a real answer.
- A senior engineer reads every brief
- NDA signed before you share anything sensitive
- No sales sequence, no automated follow ups