02 · Data Engineering & Analytics
The data foundation everything else depends on.
We design and build the pipelines, storage and metric layers that turn scattered operational data into something analysts, applications and AI models can rely on, with quality checks, lineage and cost under control.
- Data platforms
- ETL / ELT
- Data lakes
- Data warehousing
- Real-time pipelines
- Analytics
- Data quality
- AI-ready data architecture
What we build
Data Engineering & Analytics, in practice.
Platform architecture
Lakehouse and warehouse designs on Snowflake, BigQuery, Redshift or Postgres, sized to the workload rather than the vendor brochure.
Batch & streaming pipelines
Ingestion from operational databases, SaaS systems, files and devices; transformations modelled and tested in code; incremental and real-time where it matters.
Metric & semantic layers
Business metrics defined once and used everywhere: dashboards, APIs and AI assistants. Numbers stop drifting between teams.
Data quality & observability
Contracts, freshness and volume checks, anomaly alerts and lineage, so issues are caught in the pipeline rather than in a board meeting.
Analytics products
Decision dashboards and embedded analytics for operations, sales and field teams, built on governed data.
AI-ready data
Feature tables, document pipelines, embeddings and spatial joins prepared for machine learning and retrieval workloads.
Architecture
Reference data platform
How we work
Engineering principles.
- Snowflake
- BigQuery
- Redshift
- PostgreSQL
- dbt
- Apache Iceberg
- DuckDB
- Python
Model the business, not the source
Pipelines mirror how the business thinks about customers, outlets, assets and territories.
Tests in the pipeline
Every transformation is version-controlled and tested; bad data is quarantined, not silently loaded.
Cost is a design input
Partitioning, file formats and refresh cadence are chosen with the monthly bill in mind.
Start a conversation
Bring us the hard problem.
Tell us what you are trying to build and the constraints you are working within. We will come back with how we would approach it.