Raw tables feed reporting
Dashboards depend directly on source schemas, so an upstream change can alter a business number without a controlled model in between.
Data warehouse consulting
Data warehouse consulting services from architecture to delivery.
Design, build, migrate or improve a cloud data warehouse across Snowflake, BigQuery, Databricks and ClickHouse. Architecture, modelling, performance and reporting foundations follow the workload and team, not a preferred vendor.
Snowflake
Cloud data warehouse for analytics and data applications.
Our expertise
We design schemas, tune SQL, manage costs, and support Snowflake delivery.
BigQuery
Google Cloud warehouse for large-scale analytical workloads.
Our expertise
We model datasets, optimize queries, and connect BigQuery into pipelines.
Databricks
Tooling used in modern data and analytics work.
Our expertise
We apply it where it fits the team, data platform, and delivery constraints.
ClickHouse
Tooling used in modern data and analytics work.
Our expertise
We apply it where it fits the team, data platform, and delivery constraints.
dbt
Analytics engineering framework for tested SQL models.
Our expertise
We build, refactor, test, document, and review dbt projects.
Choose the platform around the workload, define the grain before the model and give reporting a stable contract above raw source data.
The problem
Where a data warehouse stops supporting the team.
The warehouse is running, but its models, workloads and reporting contracts are harder to explain with every change.
The grain is unclear
Teams join tables without agreeing what one row represents, which creates duplicate counts and difficult reconciliations.
Changes break downstream work
Warehouse changes ship without contracts, tests or a dependency review, leaving analysts to find failures after release.
A migration copies the old problems
Schemas and jobs move to a new platform, but duplicated logic, unclear ownership and expensive query patterns move with them.
What you get
Data warehouse consulting services
Vendor-neutral consulting and implementation across the warehouse lifecycle.
Data warehouse architecture
Define layers, workload boundaries, ownership and the interfaces between ingestion, transformation and reporting.
Warehouse implementation and improvement
Build or improve the warehouse your team uses, with platform choices, access and operating decisions documented.
Dimensional modelling with dbt
Create tested facts, dimensions and marts with explicit grain, lineage and reusable metric logic.
Data warehouse migration
Plan schemas, loads, transformations, validation and cutover without treating migration as a blind copy.
Cost and performance work
Use workload evidence and query profiles to tune models, compute and processing patterns.
Data quality and reporting foundations
Add tests, contracts and stable reporting layers so downstream teams can see what changed and why.
Warehouse architecture
Connect platform choice to the full reporting path.
A warehouse decision affects ingestion, workload isolation, transformation models and downstream reporting. Treat those layers as one operating system, then choose the platform that fits its constraints.
Platform follows workload
SnowflakeBigQueryDatabricksClickHouseWarehouse architecture
VENDOR-NEUTRAL01Sources and ingestion
02Warehouse and workload design
03Tested transformation models
04Metrics, BI and downstream use
- One documented row grain and key
- Transformation logic lives in version control
- Incremental processing follows the source update pattern
- Reports read a stable model instead of raw source tables
Reviewable delivery
What should be visible before sign-off.
Architecture decisions connected to implementation.
Before sign-off, the warehouse design should make workload boundaries, ownership and downstream interfaces clear. A build or migration should add explicit model grain, validation criteria, a cutover path and the operating decisions the internal team will need later.
How we work
Choose the delivery shape around the warehouse problem.
Start with an audit when priorities are unclear, a sprint for one defined outcome, or embedded support for recurring delivery.
Scoped warehouse project
A defined architecture, modelling, migration or performance outcome with acceptance criteria, implementation and handoff.
Embedded warehouse delivery
Recurring senior engineering capacity inside your repositories, account and planning cadence for a growing warehouse backlog.
Read the thinking behind the work.
Practical writing and open-source tools related to this service. Useful context, not a substitute for client results.
Questions
Answered, before the call.
Practical answers for teams scoping this kind of work. If your situation is not covered, the contact step is a short scoping call.
Discuss your data warehouse.
Share the platform, the reporting or migration problem and the outcome you need. We will reply with the most useful next step.