Data platform & warehousing
One place where the numbers agree, with lineage back to the source.
What is it?
When finance, operations and the board each have a different figure for the same measure, the problem is structural. We build the warehouse, the ingestion from source systems, and the modelling layer that makes a single definition of a metric possible and traceable.
For a technical reader
Layered modelling is what makes a warehouse maintainable: a raw landing layer kept immutable and append-only, a cleaned and conformed layer, and business-facing marts. Keeping raw immutable means a definition change is a rebuild rather than a re-ingest. Dimensional modelling with surrogate keys and slowly-changing dimensions is still the right default for analytics, because it answers 'what did this look like in March' without heroics.
Our services
What’s covered
- Cloud warehouse
- Snowflake, BigQuery, Redshift or Oracle Autonomous Data Warehouse
- Ingestion and ELT
- Scheduled and streaming loads from ERP, CRM, operational databases and third parties
- Dimensional modelling
- Facts, dimensions and slowly-changing history, so time comparisons actually work
- Transformation layer
- dbt-style versioned SQL models with tests and documentation attached
- BI connectivity
- A governed semantic layer so Power BI, Tableau or OAC all read the same definitions
Business problems we solve
Our delivery approach
- 01Agree the definitionsWhat counts as an active customer, a closed order, revenue. Mostly a business conversation, and the part that is skipped most often.
- 02Ingest reliablyScheduled, monitored, idempotent loads with clear handling for late and corrected data.
- 03Model in layersRaw, cleaned, and business-facing marts kept separate, so a definition can change without re-ingesting history.
- 04Test the dataAutomated checks on freshness, volume and referential integrity, so a broken upstream feed is caught before it reaches a dashboard.
Solutions & project types
Examples of the work we take on in this area. These describe project shapes, not named client engagements.
Technologies & platforms
Warehouse
- Snowflake
- Google BigQuery
- Amazon Redshift
- Databricks
- Oracle Autonomous Data Warehouse
Ingestion
- Fivetran
- Airbyte
- AWS DMS
- Debezium
- Custom extractors
Transformation
- dbt
- SQL
- Spark
- Dataform
Orchestration
- Airflow
- Dagster
- Prefect
BI
- Power BI
- Tableau
- Looker
- Oracle Analytics Cloud
Quality
- dbt tests
- Great Expectations
- Monte Carlo
Learn this field
Where to start with data platform & warehousing.
Secondary to the work itself, but genuinely useful if you are building the skill. Every link goes to official documentation or an official channel.
Beginner
- SQL properly: joins, aggregation, window functions
- Relational modelling and normal forms
- What a warehouse is for, versus an operational database
- Load a CSV, model it, chart it
- Source control for SQL
Intermediate
- Dimensional modelling: facts, dimensions, grain
- Slowly changing dimensions and why type 2 matters
- ELT with dbt: models, tests, documentation
- Incremental loads and late-arriving data
- Query performance: partitioning, clustering, cost
Advanced
- Semantic layers and governed metric definitions
- Data contracts between producers and consumers
- Lakehouse table formats: Iceberg, Delta
- Point-in-time correctness and bitemporal modelling
- Cost governance on consumption pricing
Official documentation
Official video channels
Certifications
- dbt Analytics Engineering Certification
- SnowPro Core Certification
- Google Cloud Professional Data Engineer
- Microsoft Certified: Fabric Analytics Engineer Associate
Practice projects
- Model a public dataset into staging and mart layers with dbt
- Add tests for uniqueness, nulls and referential integrity, then break one
- Implement a type 2 slowly changing dimension and query history
- Build an incremental model and handle a late-arriving record
- Trace one dashboard number all the way back to source
FAQs
Questions we get asked.
Warehouse, lake or lakehouse?
For structured business reporting, a warehouse is usually the straightforward answer. A lakehouse earns its complexity with large semi-structured volumes or heavy machine-learning workloads. We would rather start simple and add.
How long before the first useful output?
One well-chosen subject area, end to end, is typically achievable early. Doing that before widening proves the pipeline and the definitions.
Can we keep our existing BI tool?
Yes, and usually you should. The warehouse and semantic layer are the part that matters; the visualisation tool sits on top and can be changed later.
Who owns metric definitions?
The business, not engineering. Our role is to facilitate agreement and implement it once so it stops being re-litigated per report.
AI & Data
Related services.
GenAI assistants & copilotsAssistants grounded in your own content, with the evaluation to show whether they are actually right.Forecasting & machine learningModels that stay in production and keep being retrained, not a notebook that was accurate once.Document & contract intelligenceExtracting structured, checkable data from contracts, invoices and forms at volume.Data governance & guardrailsKnowing what data you hold, who can reach it, and what the AI systems are allowed to do with it.
Start the conversation
Talk to our experts about data platform & warehousing.
Tell us where you are. A specialist will read it and reply within one business day.