Services—Data Architecture

Data Architecture Consulting

Most data problems are architecture problems. Slow dashboards, unreliable pipelines, inconsistent numbers across reports, AI projects that stall at the data layer — these are symptoms of systems that were never properly designed. Our team includes former Microsoft data architects who built enterprise-scale data platforms from the inside. We design data infrastructure from first principles: the right storage layer for your workload, governance that teams actually follow, and a semantic layer that enforces consistent definitions across every downstream system. The difference between a well-architected data platform and a poorly architected one is not visible on day one — it becomes visible at month twelve, when one team is delivering new capabilities every sprint and the other is still fighting the same pipeline failures they inherited.

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Data architecture consulting
10+
Years architecture experience
50+
Enterprise systems designed
40%
Avg. cost reduction
5x
Typical query speedup

What's Included

Capabilities

01

Architecture Assessment & Audit

Before recommending anything, we audit what you have. That means reviewing your data model designs, pipeline architecture, governance documentation, cloud cost patterns, and the gap between your current state and what your business actually needs. The assessment deliverable is a written report covering the structural problems we found, the root causes behind them, and a prioritised remediation roadmap. Most organisations find that two or three structural changes account for the majority of their data problems — the assessment tells you which ones. It takes 2–4 weeks and gives you a clear picture of what to fix and in what order, regardless of whether you engage us for the remediation.

02

Data Warehouse & Lakehouse Design

We design data warehouse and lakehouse architectures built for analytical workloads — not repurposed transactional schemas. That means dimensional modelling (star schemas, conformed dimensions, slowly changing dimension handling), schema design optimised for the query engine you are using (Snowflake, Databricks, Synapse, BigQuery), partitioning and clustering strategies for your data volume and access patterns, and a semantic layer that enforces consistent metric definitions across all downstream systems. We choose the right architecture for your workload — not the fashionable one. A well-designed traditional data warehouse is the right answer for some environments; a medallion lakehouse is the right answer for others. We make this decision based on your data volume, latency requirements, and team capability.

03

Data Pipeline Architecture

Reliable, maintainable data pipelines are the infrastructure underneath everything else. We design ingestion patterns appropriate to your source systems and latency requirements, transformation architectures using dbt, Databricks notebooks, or Azure Data Factory, and a testing and observability framework so that pipeline failures are caught before they reach your dashboards. The most expensive data pipeline problems we see are not the ones that fail loudly — they are the ones that produce wrong numbers silently. We design pipelines with data quality checks at every layer: null rate monitoring, referential integrity validation, row count reconciliation, and schema change alerting.

04

Data Governance Framework

A governance framework that exists only in a policy document is not a governance framework — it is a risk. We design governance frameworks that are embedded in the architecture: access controls enforced at the platform layer rather than relying on human compliance, data quality rules that run automatically on every pipeline execution, a data catalogue populated from the platform rather than maintained manually, and a data ownership model with clear accountability. The output is not a slide deck. It is a working governance implementation — policies instantiated as platform configuration, automated checks, and documented standards that your team can operate.

05

Medallion Architecture

Medallion architecture (Bronze, Silver, Gold) is the dominant pattern for modern lakehouse environments and the right approach for most organisations consolidating data from multiple operational sources. Bronze receives raw, immutable data from source systems. Silver applies cleansing, conformation, and integration logic to produce a governed operational store. Gold materialises business-specific aggregates and semantic models for consumption by BI tools and AI systems. We implement medallion architectures on Azure Databricks, Snowflake, Delta Lake, and BigQuery — with full lineage tracking between layers, data quality checks at the Silver boundary, and a documented data contract for every Gold table.

06

Master Data Management

When the same customer exists in your CRM, your ERP, your billing system, and your data warehouse with four different IDs and three different spellings of their name, every report that crosses those systems is unreliable. We design and implement master data management frameworks that establish a single, trusted source of truth for your critical business entities — customers, products, locations, suppliers, employees. This includes identity resolution logic (matching and merging records from multiple source systems), a canonical master record design, a survivorship strategy for conflicting attribute values, and integration patterns that keep downstream systems synchronised with the master record.

When You Need Us

Use Cases

01/
Your data team spends more time fixing pipelines than building new capability
When your senior engineers are spending 60% of their sprint time investigating data quality failures and patching pipelines, the problem is not their skill level — it is the architecture they are working in. Brittle pipelines without proper error handling, missing data quality checks, schemas that change at source without warning, and no observability layer all compound into an environment where reactive maintenance crowds out proactive development. We conduct a pipeline architecture review, identify the structural causes of instability, and redesign the foundations so that failures are caught early, recoverable, and isolated — not cascading through your entire platform at 2am.
02/
You are building a new data platform and want to design it correctly from the start
Getting the architecture right before you build is ten times cheaper than remediating it after the fact. The most common mistake organisations make when starting a new data platform is letting the first engineers hired make all the structural decisions — pragmatically, locally, without a governing architecture. The result is a platform that works for the initial use case and becomes a constraint for every use case after it. We design the target architecture before the first line of code is written: storage layer selection, data model patterns, governance framework, pipeline standards, and the observability design — documented and agreed before implementation begins.
03/
Finance, operations, and leadership report different numbers from the same underlying data
When the revenue number in the board deck does not match the revenue number in the sales dashboard and neither matches the number in the finance system, the business has an architecture problem presenting as a people problem. The root cause is almost always a missing or broken semantic layer — metric definitions that live inside individual reports rather than in a governed, shared model. We identify every place where the same business concept is defined differently, design a canonical semantic layer with governed metric definitions, and implement it in a way that is enforced at the platform level — not dependent on every report author following a convention.
04/
Your cloud data costs are growing faster than the value you are getting
Poorly architected cloud data environments are expensive in ways that are hard to see until you look specifically for them. Full table scans on un-partitioned, un-clustered tables that run on a schedule. Compute clusters that stay warm for hours after a workload finishes. Redundant data copies persisted in multiple storage layers without a retention policy. Warehouse sizes that were set at setup and never reviewed. We run a cost architecture audit — reviewing your query patterns, compute configuration, storage tier usage, and data lifecycle policies — and identify the specific changes that will reduce spend without affecting analytical performance.
05/
Your AI and machine learning projects keep stalling at the data layer
AI and machine learning projects require data that is clean, consistently defined, accessible at the right granularity, and served at the right latency. Most enterprise data environments were designed for batch dashboards, not AI model training or real-time inference. When AI initiatives fail at the data layer — poor feature quality, inability to serve data at inference speed, no lineage for model training data — it is an architecture problem. We design data platforms that serve both analytical and AI workloads: feature store patterns, real-time serving layers, lineage tracking from source to model training, and data quality frameworks that AI systems can trust.
06/
You are migrating to a new cloud platform or consolidating after a merger
Cloud migrations and post-merger data consolidations are high-risk data architecture events. The technical work is only part of the challenge — the harder part is making decisions about what to migrate, what to rebuild, and what to retire, in an environment where the full picture of what exists is often unclear. We run a pre-migration architecture assessment, produce a complete inventory of your data assets and their dependencies, design the target state architecture on the new platform, and produce a phased migration plan that keeps the business running throughout the transition. We have run this process on Azure, Databricks, Snowflake, and BigQuery environments — and specifically in the post-merger integration scenario where you are consolidating two organisations with incompatible data architectures.

Ready to Start

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FAQ

Common Questions

What is data architecture consulting?

Data architecture consulting is the discipline of designing how data flows, is stored, structured, and governed across an organisation. A data architecture consultant assesses your current data infrastructure, identifies structural weaknesses, and designs a target state — including data warehouse schema, medallion architecture layers, governance frameworks, and data pipeline patterns. The engagement typically produces a written architecture document, a data model design, and a phased implementation roadmap. The output guides engineering implementation whether the same firm delivers it or your internal team does.

How much does data architecture consulting cost?

A data architecture assessment typically costs $15,000–$45,000 for a mid-market enterprise, delivered over 3–6 weeks. Full architecture design engagements (blueprint without implementation) run $30,000–$80,000. End-to-end design and implementation engagements — including platform build, pipeline development, and data model delivery — typically range from $80,000 to $400,000 depending on platform complexity, source system count, and governance requirements. Retainer-based ongoing architecture support typically runs $5,000–$20,000 per month. For a detailed breakdown, see our [data architecture consulting cost guide](/blog/data-architecture-consulting-cost).

What is medallion architecture and when should I use it?

Medallion architecture organises data into three progressively refined layers: Bronze (raw, immutable source data as ingested), Silver (cleansed, conformed, and integrated data), and Gold (business-ready aggregates for specific analytical use cases). It is the dominant pattern for modern lakehouse environments on Azure Databricks, Snowflake, and Delta Lake. You should use it when you are consolidating data from multiple operational source systems, when you need a clear data quality contract at each stage of transformation, and when you need full lineage from source data to business report. It is less appropriate for simple single-source environments or when your primary use case is operational reporting directly from a transactional database.

What is the difference between a data architect and a data engineer?

A data engineer builds and maintains the pipelines, transformations, and storage systems that move and process data. A data architect designs the structure, standards, and governance of the data environment that engineers build within. The architect makes the structural decisions — which storage layer, which data model pattern, what the governance framework looks like, how metrics are defined — and the engineer implements those decisions. In small organisations, the same person often does both. In larger organisations, the roles separate: architects set the direction and standards, engineers deliver to those standards. For a detailed comparison, see [data architecture vs data engineering](/blog/data-architecture-vs-data-engineering).

How long does a data architecture engagement take?

An architecture assessment takes 3–6 weeks depending on environment complexity. A full architecture design (without implementation) takes 4–8 weeks. End-to-end design and implementation for a mid-market data platform — 10–30 source systems, cloud data warehouse, BI layer — typically runs 4–6 months. Enterprise-scale platforms with complex governance, regulatory requirements, or multi-cloud architecture run 6–12 months. We provide a fixed-price proposal with a defined timeline after an initial scoping conversation, so you know what to expect before work begins.

What cloud platforms do you work with?

Our primary expertise is in the Microsoft Azure data stack — Azure Synapse Analytics, Azure Data Factory, Azure Databricks, ADLS Gen2, and Microsoft Fabric. We also work with Snowflake (across Azure, AWS, and GCP), Databricks on AWS and GCP, and BigQuery. We are platform-agnostic at the advisory level — our architecture recommendations are driven by your requirements, not by platform partnerships. Where a client has an existing investment in a specific cloud provider, we design within that context. Where the choice is open, we present the trade-offs and recommend the platform that best fits the workload.

How do I know if my data architecture needs attention?

The most reliable signals: your data team spends more time maintaining pipelines than building new capability; different business units report different numbers from the same source data; AI or analytics projects consistently stall at the data layer; cloud data costs are growing faster than data value; onboarding a new data source takes weeks or months; data quality issues surface in production rather than being caught in the pipeline. Any one of these signals is worth investigating. If you have several, the architecture is almost certainly the constraint. We offer a free 30-minute data architecture audit as a no-obligation starting point.

How do you choose a data architecture consulting firm?

Ask for evidence of production architecture work at the scale and complexity you are targeting — not just delivery references, but architecture work specifically. Ask who will be on your engagement: in large firms, proposals are made by principals and delivered by junior staff. Ask how they approach the diagnostic phase — a firm that proposes a solution before assessing your environment is a risk. Ask for references from clients in similar industries or with similar problem types. Be cautious of firms that sell a specific technology before understanding your requirements. The right architecture for your business is not a default template — it is the result of a careful diagnostic.

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