Data Architecture
We design the data foundation that makes everything else possible.
Architecture as a strategic decision
Most enterprise data problems are not storage problems. The data exists. The platforms exist. The missing piece is the architecture that connects them, and that architecture is a strategic decision, not a technical afterthought. Organisations that treat it as the latter spend years retrofitting pipelines, rebuilding data models, and wondering why their engagement platform still cannot see what their warehouse already knows. Getting the architecture right first is what separates a composable stack that compounds in value from one that accumulates technical debt.
A data layer marketing can actually use
The gap between data that exists and data that drives action is where most martech investments quietly stall. The warehouse has everything. The CDP has some of it. The engagement platform has less. Somewhere between those three systems, the single customer view that was meant to power personalisation at scale remains frustratingly out of reach. The platforms are not the problem. The architecture connecting them is.
Here is what that architecture looks like in practice.
CDP & Activation
We work across Segment, Amperity and Hightouch to build the activation layer that bridges your data foundation and your engagement platform.
Warehouse Integration
Deep experience with Snowflake, Databricks, Redshift and Microsoft Fabric — designed and connected as your single source of truth.
Identity Resolution
The logic that unifies your customer data across sources, giving every downstream platform a complete and consistent view of who it's talking to.
Industry Scale
The fourth layer of the stack is operational. We have run this architecture at enterprise data volumes across aviation, banking, utilities, wagering, multi-brand retailers, FMCG and QSR — environments where governance constraints are non-negotiable and the cost of a poorly designed data model compounds fast.
Designed before it is built
Real-time event streaming, reverse ETL and identity resolution are not bolt-ons. They are decisions that need to be made early and made correctly, because the cost of redesigning a data model downstream is significant. We work from first principles, documenting every key design decision before a connection is built, so your architecture reflects your actual business requirements rather than the defaults your platforms shipped with.
Every engagement produces a documented blueprint your team can own, interrogate and evolve, not a black box that only we understand. We work alongside your engineering and data teams throughout, which means the people responsible for maintaining the architecture are the same people who helped design it.
Built for the activation layer
The output of good data architecture is an activation layer that actually fires. For our clients, that activation layer is Braze, and our data architecture work is specifically designed to ensure Braze has the right data, at the right fidelity, to support the personalisation and automation use cases that justify the investment. We build the infrastructure with the end state in mind. We know what good looks like because we are also the people who activate against it.
How an engagement works
Data Architecture engagements follow a four-phase model built around one principle: the decisions that matter most happen before anything is built.
Discovery and Audit
We map your current data landscape in full: sources, destinations, identity logic, data quality, governance constraints, and the gaps between what your platforms receive and what they actually need. This phase surfaces the architectural decisions that have to be made before design begins.
Architecture Blueprint
Discovery outputs become a documented architecture blueprint, aligning the core design decisions, integration approach and platform recommendations before build begins.
Build and Integrate
We execute against the signed-off blueprint: standing up connections, configuring pipelines, implementing identity resolution, and integrating across your CDP, warehouse and engagement stack. We work in your environment, with your team, with full documentation at every stage.
Validate and Activate
We test against real data, validate fidelity at the activation layer, and ensure your team understands what has been built and why. You leave with a data architecture that works, documentation that explains it, and the confidence to extend it.
Ready to compose your future?
Let's talk about how we can help you build a modern martech stack that drives real results.