Ingestion and pipelines
Batch and streaming ingestion from POS, ecommerce, ERP, CRM and partner feeds, with schema handling, retries and alerting that names the failing source.
Data & Analytics practice
The unglamorous work that makes everything else possible
Dashboards nobody trusts and AI pilots that stall usually share one cause. We build the pipelines, models and governance underneath so the numbers agree with each other - and stay that way.
What we build
A platform, not another extract
Warehouse and lakehouse
A layered model - raw history, conformed core, curated marts - so a change in a source system does not ripple straight through to a board report.
Semantic model
One definition of margin, of a return, of an active customer. Written down, version controlled and used by every report instead of re-derived in each one.
Reporting and dashboards
Views built for a decision, not for completeness. Fewer charts, each one answering a question somebody named before we started building.
Quality and observability
Tests on freshness, volume, uniqueness and referential integrity, running with the pipeline - so problems are found by the platform, not by a director on Monday.
Governance and cost control
Lineage, access control, retention and a view of what each workload costs to run. Cloud data bills grow quietly until somebody makes them visible.
Signs this is your problem
You probably recognise at least two of these
- Two teams present different revenue numbers for the same month and both can defend theirs
- A monthly report depends on a spreadsheet only one person knows how to refresh
- Nobody can say where a figure came from without opening four systems
- An AI or forecasting initiative is stuck waiting on "getting the data ready"
- Pipelines fail silently and are discovered when a dashboard looks wrong
- Adding a new store, brand or channel means editing a dozen jobs by hand
- Your cloud data bill has grown faster than your data volumes
- Analysts spend most of their week preparing data instead of analysing it
Approach
Value in weeks, foundation in parallel
We do not disappear for six months to build a platform. The first useful output lands early, and the foundation is laid underneath it as we go.
Map the estate
Sources, owners, current reports, the spreadsheets in between, and where the disagreements actually come from. Usually two weeks.
Pick a first decision to serve
One report or metric that matters enough that its accuracy is worth arguing about. That becomes the first vertical slice through the whole stack.
Build the slice properly
Ingestion, model, tests, dashboard and documentation for that one decision - built the way everything else will be built.
Widen deliberately
Each additional domain reuses the same patterns. Speed increases as the shared layer grows, rather than the estate becoming harder to change.
Hand over or run it
Your team takes it on with documentation and pairing, or we operate it under an agreed support model. Both are fine; drifting into the second by accident is not.
Toolchain
Common building blocks
- SQL
- Python
- Airflow
- dbt-style modelling
- Kafka & event streams
- Spark
- Snowflake
- BigQuery
- Redshift
- Databricks
- AWS Glue & S3
- Postgres
- Power BI
- Tableau
- Looker
- Data quality testing
Start with an honest assessment
A short engagement that maps what you have, what it costs and what it would take to make the numbers agree. You keep the findings either way.