›› Free Data Maturity Scorecard — Discover your analytics score in 3 minutes MY SCORECARD →
Data Engineering

Data Warehouse Consulting That Gives Every Team One Source of Truth

Numlytics delivers expert data warehouse consulting for enterprises across the US, UK, Australia & UAE. We design and implement cloud data warehouses on Snowflake, Azure Synapse, Microsoft Fabric, and Databricks - with dimensional modelling, star schema design, and a semantic layer your BI team can trust. From greenfield builds to legacy migration, production-ready delivery guaranteed.

Get Free Consultation ← Back to Data Engineering
Dimensional modelling & star schema design included as standard
Greenfield builds, migrations, and legacy modernisation
BI-ready semantic layer from day one
Up to 50% lower cost vs US/UK data engineering firms
Delivery Facts
15+
Cloud DW migrations
with zero data loss
10×
Avg. query performance
improvement post-build
4wk
First data mart live
within 4 weeks
50%
Lower cost vs US/UK
engineering firms
We build on
Snowflake
Microsoft Fabric
Azure Synapse
Databricks Lakehouse
dbt
Google BigQuery
Amazon Redshift
Power BI Semantic Layer
What We Build

A Data Warehouse That Everyone Trusts and Uses

A data warehouse is only valuable if the data inside it is trusted. Most organisations have built something that technically holds data, but that analysts distrust, business users ignore, and developers are afraid to change because nobody documented how it works or why it was built the way it was.

Our data warehouse consulting builds on proven methodology - Kimball dimensional modelling, star schema design, conformed dimensions, and a clear semantic layer that maps business concepts to technical structures. Every data warehouse design we deliver is documented, tested against production-volume data, and built for the BI and analytics tools your team actually uses.

We work across greenfield builds, legacy migration to cloud, and performance modernisation of existing warehouses, on Snowflake, Microsoft Fabric, Azure Synapse, and Databricks.

Design Your Data Warehouse →
Why Clients Come to Us
"Nobody trusts the numbers in our data warehouse"
Metrics are defined differently in different schemas. Reports built by different teams return different answers to the same question. Trust collapsed a long time ago.
"Queries that should take seconds take 10 minutes"
No partitioning, no clustering, no materialisation strategy. Every dashboard query scans the full table. The warehouse was never designed for the data volumes it now holds.
"We're moving to the cloud and need to rebuild our DW"
On-prem SQL Server or Oracle DW that needs to be migrated to Snowflake, Fabric, or Synapse — but the existing schema design doesn't map cleanly and needs to be redesigned.
"We built a data lake but BI tools can't query it effectively"
Raw data in a lake is not queryable by Power BI or Tableau at scale. A structured data warehouse or lakehouse serving layer is needed between the raw data and the BI layer.
What We Deliver

Six Components of Every Data Warehouse We Build

Every engagement delivers these six components - designed together as a coherent, documented, production-ready data warehouse.

Dimensional Modelling & Schema Design

Kimball-methodology >dimensional modelling - fact tables, dimension tables, conformed dimensions, and slowly changing dimension (SCD) strategies, designed to serve BI queries at scale with consistent, agreed metric definitions.

Star schema & snowflake schema design
SCD Type 1, 2 & 3 strategies
Conformed dimensions across subject areas
Warehouse Architecture & Platform Selection

We design the right cloud data warehouse architecture for your data volumes, query patterns, and BI requirements - selecting between Snowflake, Azure Synapse, Microsoft Fabric, or Databricks based on your existing stack, not vendor preference.

Platform evaluation & selection framework
Storage & compute architecture design
Cost modelling per platform option
Data Ingestion & Pipeline Layer

The ingestion and transformation pipelines that populate your warehouse, ETL/ELT pipelines built in Azure Data Factory or Databricks, plus a dbt transformation layer that models raw data into your dimensional schema with full testing and lineage.

Batch & incremental load patterns
dbt models for transformation layer
CDC & historical load strategies
Performance Optimisation

Query performance engineering built into every warehouse we design - clustering keys, partitioning strategies, materialised views, result caching, and virtual warehouse sizing for Snowflake, or distribution keys and indexes for Synapse and Redshift.

Clustering & partitioning strategy
Materialised view & caching design
Query profiling & execution plan review
Semantic Layer & BI Readiness

A semantic layer that maps your dimensional schema to business-friendly metric definitions - Power BI semantic models, Analysis Services cubes, or dbt metrics, so analysts build consistent reports on a single, trustworthy calculation layer.

Power BI semantic model design
Business metric definitions & DAX
Row-level security implementation
Governance, Security & Documentation

Data governance baked into the warehouse - role-based access control, column-level security, data lineage via dbt or Microsoft Purview, and full technical documentation so your team can maintain, extend, and trust what we've built indefinitely.

RBAC & column-level security
dbt documentation & lineage graph
Architecture & runbook documentation
How We Deliver It

From Requirements to BI-Read Warehouse in 4 Phases

First data mart live in 4 weeks. Sprint-based delivery with weekly demos, no big reveal at the end.

Requirements & Data Discovery

Stakeholder interviews to capture reporting requirements, business metrics, and data sources. We audit your existing data landscape, source system schemas, and data volumes before designing anything.

⏱ Weeks 1–2
Architecture & Schema Design

Platform selection, dimensional model design, star schema blueprints, SCD strategies, and semantic layer architecture - all agreed and documented before build begins. No surprises in sprint 3.

⏱ Weeks 2–3
Sprint Build & Validation

Weekly sprints - each delivering a tested, documented data mart. Pipelines, dbt models, semantic layer, and Power BI connections all validated against production data volumes before each sprint closes.

⏱ Weeks 4 onwards
Cutover, Training & Handover

Zero-downtime cutover with full data validation at cutover point. Team training on the warehouse model, dbt, and platform tooling. Full documentation so your team owns and extends what we've built.

⏱ Final sprint
Why Numlytics

Why Choose Numlytics for Data Warehouse Consulting

We've built and migrated data warehouses for enterprises across manufacturing, financial services, SaaS, and retail - in the US, UK, and Australia.

Kimball Methodology, Applied Correctly
We use proven dimensional modelling methodology - star schema, conformed dimensions, SCD strategies, not ad-hoc schema design that looks fine until report numbers start conflicting across teams.
Platform-Agnostic Recommendation
We work across Snowflake, Fabric, Synapse, Databricks, BigQuery, and Redshift. Our platform recommendation is based on your requirements, not vendor partnerships or certifications we're incentivised to push.
Performance Built In, Not Retrofitted
Clustering, partitioning, materialised views, and semantic layer caching are designed from the start, not added later when dashboards become too slow. Our warehouses perform at 10× the speed of what clients typically replace.
End-to-End: Pipelines to BI Layer
We deliver the full stack - ingestion pipelines, dbt transformation layer, dimensional warehouse, semantic model, and Power BI connection. One team, no handoffs, no gaps between what the warehouse produces and what BI needs.
Zero Data Loss — Every Migration
We have completed 15+ cloud data warehouse migrations with zero data loss at cutover. Every migration includes full data validation at each pipeline stage before the old system is decommissioned.
Up to 50% Lower Cost
Certified offshore data engineers from India, the same warehouse design quality as top US or UK data engineering firms at up to 50% lower cost, with full timezone overlap and daily standups throughout.
★★★★★

"We had an on-prem SQL Server data warehouse that had been growing organically for nine years. No consistent schema, no documentation, queries taking 8–15 minutes, and analysts maintaining their own shadow spreadsheets because they didn't trust the DW numbers. Numlytics audited the entire estate, redesigned the dimensional model from scratch, migrated it to Snowflake, and built the semantic layer in Power BI. We went from 12-minute average query times to under 45 seconds. The team actually uses the warehouse now - and for the first time in years, Finance and Sales agree on the revenue number."

SB
Sarah B.
Director of Analytics · Financial Services, United Kingdom

The data warehouse is the foundation. These services build on top of it.

ETL Pipeline Development
The pipelines that load data into your warehouse
→
Data Lakehouse Architecture
When you need both warehouse and data lake in one
→
Data Quality Management
Quality validation inside and around your warehouse
→
Power BI Consulting
The BI layer that sits on top of your warehouse
→
All Data Engineering Services ↗
Full data engineering service hub
→
FAQ

Data Warehouse Consulting FAQs

Common questions before starting a data warehouse engagement with Numlytics.

Ask Us Anything →
Data warehouse consulting involves designing and building a centralised data repository that consolidates data from multiple sources into a structured, query-optimised format. A consultant handles dimensional modelling, platform selection, pipeline design, semantic layer architecture, and performance optimisation - delivering a single source of truth your BI tools can query reliably at scale.
It depends on your stack. On the Microsoft ecosystem, Microsoft Fabric or Azure Synapse is usually the best fit. For platform-agnostic cloud warehousing with excellent scaling, Snowflake is our most common recommendation. Databricks Lakehouse suits organisations needing both warehouse and ML workloads on the same platform. We evaluate your requirements and recommend accordingly - we have no vendor incentives.
Numlytics delivers the first data mart within 4 weeks. Full enterprise data warehouse builds with multiple subject areas typically run across 8–20 week sprint engagements - with a tested, documented data mart delivered each sprint. You see working output every week, not a big reveal at the end of a long engagement.
Yes, data warehouse migration is one of our most common engagements. We assess your existing schema, design the target cloud architecture, and execute the migration in sprints with zero-downtime cutover and full data validation at every stage. We have completed 15+ cloud DW migrations with zero data loss. See our data engineering services for the full scope.
Dimensional modelling - the Kimball methodology, organises data into fact tables (measurements) and dimension tables (context) connected in a star schema. It matters because it makes queries fast, intuitive for BI tools, and consistent across teams. Most data trust problems we see stem from warehouses built without proper dimensional modelling - resulting in conflicting metrics and poor query performance.
Ready to Start?

One Version of the Truth - Your Whole Organisation Can Trust

Get expert data warehouse consulting - dimensional modelling, platform selection, pipeline build, and semantic layer, delivered by certified data engineers. Proposal in 24 hours. US, UK, Australia & UAE.

Get Free Consultation ← All Data Engineering Services
Other Data Engineering Services
ETL Pipeline Development Data Lakehouse Architecture Real-Time Data Streaming Data Quality Management Business Intelligence