Data & Analytics
Snowflake Data Engineering
A modern data platform your organisation can trust.
The challenge
Where teams get stuck.
Design, migration and engineering of Snowflake data platforms, with tested pipelines, sound governance and cost under control from the start.
- 01
Data is scattered across systems and reports disagree with each other.
- 02
A legacy warehouse is costly, slow and difficult to scale.
- 03
Pipelines fail silently and data quality issues are found by the business first.
- 04
Snowflake spend is growing without a clear view of what is driving it.
What we deliver
How we help.
Platform architecture & setup
A well-structured Snowflake foundation covering accounts, environments, roles, security and infrastructure as code.
Migration to Snowflake
Planned migration from on-premise and legacy cloud warehouses, with automated reconciliation to prove nothing was lost.
ELT pipelines & data modelling
Reliable ingestion and transformation with version-controlled, modular models that analysts can understand and extend.
Data quality & testing
Automated tests, freshness checks and observability on every pipeline, bringing software testing discipline to your data.
Governance & security
Role-based access, masking, classification and lineage so sensitive data is protected and auditable.
Performance & cost optimisation
Warehouse sizing, query tuning and usage monitoring to keep performance high and consumption predictable.
Outcomes
What you can expect.
- A single, trusted source of data for analytics and AI
- Pipelines that are tested, monitored and recoverable
- Governed access to sensitive data
- Predictable, well-understood platform costs
Technologies we commonly work with
We are tool-agnostic and work with your existing stack. These are typical for this kind of engagement.
- Snowflake
- dbt
- Snowpark
- Snowflake Cortex
- Fivetran
- Airflow
- Terraform
- Power BI
- Tableau
Questions
Common questions.
We already use Snowflake. Can you still help?
Yes. Many engagements start with a health check of an existing platform, covering architecture, security, pipeline reliability and cost, followed by targeted remediation.
How do you make sure migrated data is correct?
We build automated reconciliation between source and target as part of the migration, comparing counts, aggregates and samples, so sign-off is based on evidence rather than spot checks.
Let's talk about your next project
Tell us what you are trying to achieve. One of our engineers will listen, ask useful questions and tell you honestly whether we can help.