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Ingestion, a governed warehouse or lakehouse and a semantic layer, engineered with tests, lineage and access control, so every team reads the same number and your own staff can run the platform after we leave.
Your reports come from spreadsheets stitched together by hand, and nobody trusts the totals.
Sound familiar?
You need ingestion, modelling and serving built like software: version control, CI, data contracts, lineage and fine-grained access, on a platform your team can operate.
Sound familiar?
What you can hold the work to, in plain terms.
Metrics defined once in a semantic layer and used by every dashboard, API and notebook.
Tests, freshness checks and data contracts stop bad data before it reaches a report.
A catalog, lineage and role-based access, with personal data classified and protected.
Code in Git, CI for every change, documentation and training, so the platform outlives the project.
Data moves through layers of increasing trust, and governance runs underneath every one of them.
Sources (databases through change data capture, SaaS APIs, files and events) are ingested in batch or streaming with schema checks. The lakehouse holds bronze (raw, append-only, replayable), silver (cleaned, typed, conformed) and gold (business-ready models) layers, governed by a catalog with lineage, tests and contracts, access policies, and freshness and cost monitoring. Data is served to dashboards, data APIs and machine-learning features. Orchestration uses Airflow or Dagster schedules, dbt builds with tests, CI for every model change and a semantic layer for shared metrics.
The disciplines inside this service, and the detail we work to in each.
01
The questions the business needs answered, turned into a model that can answer them, and a roadmap ordered by value.
02
Reliable movement from every source, in batch or in real time.
03
Storage and compute chosen for your volumes, skills and budget.
04
Transformations written as tested, reviewed, versioned code.
05
Know what data exists, where it came from and who may see it.
06
Trusted data delivered where decisions are made, from the boardroom dashboard to the feature store behind a model.
The platform is chosen after we have seen your sources, your volumes and your team, not before. If what you run today is sound, we build on it.
| Platform | Fits when | Strength | Watch for |
|---|---|---|---|
| PostgreSQL | Fits whenVolumes are modest and the team already knows SQL. | StrengthLow cost, simple to run, one familiar engine. | Watch forHeavy analytical queries need their own replica or instance. |
| Snowflake | Fits whenA SQL-first team wants elastic compute with little administration. | StrengthStorage and compute scale separately, per workload. | Watch forCredit spend needs budgets, auto-suspend and monitoring. |
| Databricks | Fits whenLarge volumes, streaming and data science share the same data. | StrengthDelta Lake tables, Spark and notebooks in one platform. | Watch forMore engineering depth is needed to run it well. |
| BigQuery | Fits whenYou run on Google Cloud, or workloads are bursty. | StrengthServerless: no clusters to size or patch. | Watch forOn-demand cost follows bytes scanned, so partitioning and clustering matter. |
Whichever platform it is, models live in version control beside their tests, so the business logic is never locked inside one vendor.
Every stage ends with a document you keep and a gate you can check.
Sources, owners and the definitions that matter.
Exit gate: Every key metric has one definition and one owner.
You receiveData model and definitions
Pipelines and models, tested and in version control.
Exit gate: Models pass their tests in CI before they reach production.
You receivePipelines in production
Dashboards, APIs and a semantic layer for each team.
Exit gate: New numbers reconciled with the old ones and signed off by the business.
You receiveDashboards and data APIs
Your team runs it, with our documentation and training.
Exit gate: Your team has shipped a change on its own, with us alongside.
You receiveDocumentation and training
Written down before work starts, so nothing is assumed.
The public frameworks we measure the work against, and the platforms we run it on.
Client cases name the industry and the stack, never the client.
Proof of concept on the bank’s own data platform
13 months of DevOps, back end and data work on a serverless AWS data lake
A fixed, small first engagement, then the model that fits the rest.
A first engagement
A review of your sources, reports and current pipelines, ending in a target architecture and a first use case worth building.
Dedicated team
Long programs such as a platform migration
A monthly rate per engineer
Time and materials
Ongoing improvement, support and discovery work
Billed for the time used
Fixed price
Defined projects: a website, an assessment, a migration stage
One price for that scope
A warehouse suits structured reporting at modest volume; a lakehouse suits mixed data, larger volumes and machine learning on the same storage. Many teams start with a warehouse and grow into open table formats. The assessment recommends one.
Yes. Existing warehouses, BI tools and pipelines are kept where they work; tests, lineage and structure are added around them before anything is replaced.
AI needs what analytics needs: clean, documented, governed data. The gold layer and the semantic layer serve dashboards and models alike.
Your team. The code sits in your repositories, and the handover includes runbooks and training.
Old and new run side by side. Row counts, totals of key measures and each key metric are reconciled table by table, within a tolerance agreed with you, and the business owner signs off each area before the old report is retired.
Often not. If your current warehouse is sound, we add tests, definitions and monitoring to it rather than moving you.
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The same engineers answer for each of these.
Tell us what you run and what is getting in the way. You get a reply within 24 hours.
