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Data Engineering & BI

One set of numbers everyone agrees on

Pipelines, warehouses and dashboards built on data you own outright — so reporting doesn't depend on a vendor's export button or somebody's spreadsheet.

How we approach it
  1. 1Define
  2. 2Ingest
  3. 3Model
  4. 4Visualise
  5. 5Govern

You probably need this if…

  • Two departments present different figures for the same month.
  • Reporting means exporting to Excel and rebuilding the same pivot every week.
  • Your data lives across ERP, e-commerce, POS and a CRM with no common view.
  • Leadership decisions wait days for a number that should be live.
  • Your BI is locked inside a vendor platform you can't extract from.
Data Engineering & BI

What's in scope

Pipelines and ingestion

ETL and ELT from ERP, POS, e-commerce, CRM, spreadsheets and third-party APIs, scheduled and monitored, with failure alerting.

Warehouse and modelling

A dimensional model with defined grain, documented business logic, and a single agreed definition for each metric — so “revenue” means one thing across the company.

Master data management

Deduplication, matching and governance for customers, suppliers, products and chart of accounts across entities.

Dashboards and reporting

Operational and executive dashboards in Metabase, Power BI or your existing tool, with drill-through to transaction level.

Data quality

Automated tests on freshness, completeness and referential integrity, with alerts when something upstream breaks.

Lineage and documentation

Every metric traceable back to its source table and transformation.

Method

How we approach it

  1. 1

    Define

    Agree the twenty metrics that actually drive decisions.

  2. 2

    Ingest

    Build reliable, monitored pipelines from each source.

  3. 3

    Model

    A warehouse layer with documented, tested business logic.

  4. 4

    Visualise

    Dashboards built for the decision, not for the demo.

  5. 5

    Govern

    Ownership, quality tests and a change process per metric.

What you keep

Yours at the end of the engagement

  • Pipeline code in your repository
  • The warehouse schema and models
  • A metric dictionary
  • Dashboards you can edit
  • Data-quality test suites
  • Lineage documentation

Open formats throughout — no proprietary lock-in.

Track record

Relevant experience

Large-scale data pipeline engineering for reporting and BI, including experience from Rakuten (Japan).

Metabase and Power BI reporting layers built over live Odoo accounting and operations data.

ETL and automation tooling (n8n, Meltano) connecting Odoo to CRM and AI workloads.

FAQ

Common questions

Can't we just report from inside the ERP?

For operational reports, often yes. Once you need to combine ERP with POS, e-commerce or CRM data, or report across entities, the ERP stops being the right place to do it.

Which BI tool should we use?

We're not resellers, so we've no stake in the answer. Metabase covers most needs at no licence cost; Power BI makes sense where you're already in the Microsoft estate.

Who owns the data?

You do, entirely — in open formats, in your infrastructure, with the pipeline code in your repository. That's a design constraint, not a bonus.

Keep reading

Related services

Bring us your three most argued-about numbers. We'll trace them to source and show you why they disagree.