Data platforms people trust, from ingestion to the board pack.

We build and modernise data platforms on Snowflake, Databricks and the cloud-native stack, with the modelling, testing and lineage that make the figures defensible.

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Sound familiar?

Reports that disagree depending on who ran them.
Pipelines that break silently and get fixed by hand.
A warehouse nobody can rebuild or explain.

How we approach it

Four stages, each ending with something your team can use.

Discover

Audit sources, pipelines, models and the decisions they feed. Find what is trusted and what is not.
Design

Choose the platform and the modelling approach, Data Vault, dimensional or both, and agree data contracts.
Build

Ingestion, transformation and tests as code, with lineage and quality checks built in.
Operate

Monitoring, cost controls and documentation, then a hand-over your team can run with.

What we deliver

Data strategy and platform architecture
Snowflake, Databricks and BigQuery
Batch and streaming ingestion
dbt modelling and Data Vault 2.0
Data quality, lineage and access control
Machine learning enablement

Tools we use most

Snowflake
Databricks
dbt
Airflow
Spark
Kafka

Consumer intelligence

A reproducible data platform for global trend intelligence

After a discovery phase, we designed and built WGSN's new data platform on AWS and Snowflake and proved it end to end with a social-media trend product.

WGSN
Public sector

A national-security data platform, built in six weeks

We built a platform to ingest, model and search petabytes of investigation data, cutting lead times from weeks to hours.

UK government agency
Financial services

A multi-tenant data ingestion and streaming framework on AWS

We rebuilt NewDay's infrastructure code as reusable modules and delivered an event-driven service that loads data into Snowflake.

NewDay