Data Modernization
A platform your analysts trust by default
Warehouses fail on trust long before they fail on scale. We rebuild the pipeline with tested transformations, documented lineage and contracts at the ingestion edge, so numbers stop being argued about.
Capabilities
What this actually includes
The concrete pieces of work, so you can tell what you are buying rather than inferring it.
Warehouse architecture
Dimensional or wide-table models chosen for how your people actually query, with cost characteristics understood up front.
Pipeline engineering
Incremental, idempotent transformations that can be re-run safely and recover from partial failure without manual repair.
Data contracts
Schema and semantic expectations enforced at ingestion, so upstream changes surface as a failed check rather than a wrong dashboard.
Lineage and cataloguing
Column-level lineage from source to dashboard, so any number can be traced to its origin.
Quality monitoring
Freshness, volume, distribution and null-rate checks with alerting on the tables that matter most.
Governance and access
Row and column-level security, PII classification and audited access aligned to your compliance posture.
How we work
The sequence we follow
Audit
We trace the numbers people already distrust back to their source and document what actually broke.
Model
Agree the core entities and metric definitions with the people who use them, before building anything.
Rebuild incrementally
New pipeline runs in parallel with the old until outputs reconcile — no big-bang cutover.
Contract the edges
Ingestion checks and data contracts so the next upstream change is caught in minutes, not quarters.
Hand to the analysts
Documentation, lineage and enablement so the platform is self-serve rather than a ticket queue.
Outcomes
What good looks like
Illustrative targets from engagements of this shape. Yours get agreed up front and measured.
0%
of core tables under automated quality checks
0%
reduction in warehouse compute spend after remodelling
0%
of published metrics with documented lineage
Toolkit
What we build with
Chosen per engagement against your constraints — never a house stack applied regardless of fit.
Warehouses
- Snowflake
- BigQuery
- Databricks
- Redshift
- Postgres
Transformation
- dbt
- Spark
- SQLMesh
- Airflow
- Dagster
Quality
- Great Expectations
- Elementary
- Data contracts
- Freshness SLAs
Access
- Looker
- Metabase
- Superset
- Semantic layers
Questions
Things clients ask first
Usually not. Most of the value is in the modelling and the contracts, both of which are portable. We only recommend a platform move when the cost or capability gap is genuinely material.
Keep exploring
Related capabilities
Tell us what you are trying to build
A short call with an engineer, not a sales team. If we are not the right fit we will say so and point you somewhere better.