Data Foundation
Data Engineering & Platforms
Overview
Data engineering is the layer every analytics dashboard, model and agent depends on. We build platforms that are boring in the best way: pipelines that recover on their own, models the business recognises, and costs someone actually owns.
Outcomes we measure
- Pipeline reliability and time to recover
- Cost per terabyte processed and stored
- Time from a new source to a trusted table
- Adoption by analytics, ML and agent workloads
Problems we solve
Sound familiar?
- 01
Pipelines break silently and take days to recover, so nobody trusts Monday's numbers.
- 02
Storage and compute costs climb every quarter without a clear owner or budget.
- 03
Onboarding a new source takes months, which stalls every analytics and AI use case behind it.
- 04
Teams rebuild the same metrics in different tools and argue about which one is right.
What we deliver
Concrete deliverables, not slideware.
Lakehouse and warehouse platforms
Databricks, Snowflake, BigQuery or Microsoft Fabric, on open table formats such as Iceberg and Delta.
Batch and streaming pipelines
Ingestion and transformation with dbt, Spark, Kafka, Airflow and Dagster — versioned, tested and orchestrated.
Data modelling
Dimensional and Data Vault models designed with the business, documented and tested.
Quality and observability
Data contracts, CI tests, freshness and volume monitoring, and end-to-end lineage.
How it works
From first call to production.
- 01
Platform assessment
Review current pipelines, cost, reliability and the gaps that are blocking your use cases.
2–3 weeks
- 02
Target architecture
A practical design your team can run, with a migration path that keeps the lights on.
1–2 weeks
- 03
Build in increments
Each sprint ships production pipelines with tests and documentation.
6–12 weeks per increment
- 04
Hand over or stay
Your engineers own the platform, with optional operations support from us.
Ongoing, optional
Tech & frameworks
Tools we use — in your stack.
- Databricks
- Snowflake
- BigQuery
- Microsoft Fabric
- Apache Iceberg
- Delta Lake
- dbt
- Apache Spark
- Apache Kafka
- Airflow
- Dagster
Related projects
Where we've done this.
Case studies
Case studies for this service are being prepared. Ask us for references on a call.
FAQ
Frequently asked questions
Data Foundation
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Scope it with a senior practitioner
Book a 45-minute readiness call.
Where you are, what to fix first, and what it would take.