Solution
Enterprise Data Platform
Building the ingestion, transformation, semantic, and access layers that turn siloed enterprise data into a shared, trusted, documented resource that teams can actually use.
The problem
Data that exists but can't be used
Most enterprises have been collecting data for years. It lives in ERP systems, CRM platforms, operational databases, SaaS tools, files, and spreadsheets. The problem isn't the absence of data — it's that the data is not connected, not governed, and not trusted.
Analysts spend the majority of their time extracting and reconciling data rather than analysing it. Different teams produce different numbers for the same metric, leading to disputes that slow decision-making. Pipeline failures go undetected until someone notices a downstream report is wrong. Nobody knows who owns which dataset, and there's no documentation of what any field actually means.
A data platform solves these problems structurally. It's not a single tool — it's an architecture covering ingestion, transformation, quality, semantic definition, and access that, when built correctly, makes data a reliable shared resource rather than a source of uncertainty.
Architecture
What a modern data platform looks like
Four layers, each with a distinct responsibility. The platform works when all four are present and connected.
Ingestion
Data arrives from source systems — databases, SaaS platforms, APIs, files, streaming sources. Ingestion layer handles extraction, scheduling, change detection, and raw storage without transformation.
Transformation
Raw data is cleaned, standardised, and modelled into dimensional or entity-centric structures. dbt is the primary tool for defining, testing, and documenting transformations as code with lineage tracking.
Semantic Layer
Business metrics and dimensions are defined once and published as a consistent interface for consumers. Analysts and BI tools query the semantic layer rather than raw tables — ensuring everyone uses the same metric definitions.
Access
Data consumers access the platform through BI tools, SQL interfaces, notebooks, or APIs. Access controls govern who can query what. The data catalogue makes datasets discoverable and documents ownership and meaning.
Deliverables
What we deliver
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Data platform architecture design
Component selection and integration design across all four platform layers, based on existing data sources, team skills, and analytical workload requirements.
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ELT pipeline build
Production-grade ingestion pipelines connecting all priority source systems, with monitoring, error alerting, and automatic retries — not scripts that require manual intervention when something breaks.
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Data warehouse or lakehouse
Storage and compute layer designed for the organisation's data volume, query patterns, and budget — whether Snowflake, BigQuery, Databricks, or an open-format lakehouse architecture.
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dbt transformation layer
A documented, tested, version-controlled set of dbt models covering staging, intermediate, and mart layers — with documented lineage from source to serving table.
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Semantic and metric layer
Business metric definitions implemented once in a semantic layer — so revenue, retention, and conversion mean the same thing in every tool and every report.
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Data quality and catalogue
Automated data quality checks at ingestion and transformation layers, a data catalogue with dataset ownership and field-level documentation, and alerting when quality thresholds are breached.
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BI integration
Connecting the platform to the organisation's BI tools, with dashboard templates, access controls, and performance optimisations that make reports fast and the underlying queries maintainable.
Context
Services and technology
Services involved
Warehouses & lakes
Orchestration & transformation
Industries
The data platform pattern applies across all sectors. Specific compliance requirements (HIPAA, SOX, GDPR) and data volume characteristics vary by industry and are factored into the architecture.
Common questions
Frequently asked questions
We start with a data landscape assessment — a structured two to four week exercise that maps all material data sources, the current state of any existing pipelines, the analytical use cases that matter most to the business, and the team's existing skills. From that, we produce a sequenced build plan. We almost always start by building one complete, production-grade pipeline for the highest-priority use case rather than designing the entire platform first. This gets something real in production quickly and surfaces the practical constraints that shape the rest of the architecture.
Not necessarily. Some clients engage us as their first data engineering resource. In that case, we build the platform alongside internal stakeholders and business analysts, and the engagement is structured to help the organisation build internal capability as we go. For organisations that have data engineers already, we work as an extension of that team — bringing platform architecture experience and accelerating delivery of components the internal team doesn't have bandwidth to build.
Data governance — ownership, access control policy, data classification, retention — is an organisational function, not a technical one. We design the platform to enforce governance policies through technical controls (row-level access, column-level masking, audit logging), but the policies themselves are defined by the organisation. We help design the governance model and build the tooling that implements it, including a data catalogue with dataset ownership, field documentation, and stewardship assignments. We don't assume a governance model exists before we start — establishing one is part of the platform build.
A data warehouse (Snowflake, BigQuery, Redshift) stores structured, processed data optimised for SQL-based analytical queries. It's well-suited to BI workloads and has strong governance tooling. A lakehouse (Databricks, Delta Lake, Apache Iceberg on cloud storage) stores both raw and processed data in open formats, supporting structured queries alongside machine learning workloads on unstructured or semi-structured data. The choice depends on your workloads — most analytics-only use cases are well-served by a warehouse; organisations running ML at scale or needing to retain raw data at large volume typically benefit from a lakehouse architecture.
The first production pipeline serving a real analytical use case is typically live within six to ten weeks. That's not the full platform — it's the first prioritised slice of it, delivering value while the rest is being built. We structure the build sequentially by use case priority rather than by layer, so each phase delivers something that analysts can use. A complete platform with multiple source domains, a full dbt transformation layer, and a semantic layer is typically an eight to eighteen month build depending on data estate complexity.
Ready to make your data usable?
Start with a conversation about your current data landscape — what you have, where it lives, and what you're trying to do with it. We'll map a path from there.