Data cleaning
Duplicates, inconsistent formats, empty fields and input errors, fixed with rules that are applied the same way every time.
Data engineering
Data engineering consulting for reports you can trust
Most of the work behind a good dashboard is invisible: cleaning, joining and modelling the data so the numbers add up. We take care of that layer with automated ETL pipelines that connect your ERP, your CRM and your spreadsheets, so reports refresh on their own and nobody has to patch figures by hand.
ToolsSQL, PostgreSQL, Python, RDeliverableAutomated pipelinesQualityChecks at the sourceMethodShort sprints
What we do01 02 03 04 05 06
From scattered data to a single model
Data integration
ERP, CRM (Salesforce, HubSpot), databases and spreadsheets brought together with shared identifiers.
Data modelling
Star schema models built for analysis: faster reports and consistent figures across departments.
Data migration
When you change systems, we move and validate your data so you lose neither history nor quality.
ETL pipelines
Automated processes that extract, transform and load your data as often as you need.
Reporting automation
Reports generated straight from the model. See dashboards.
Why it matters
No clean data, no BI and no AI
A predictive model or a dashboard is only as good as the data feeding it. If every source names the same customer or product differently, the figures will not match and nobody will trust the report.
That is why every project we run starts with a data audit and clean-up, including our predictive modelling package.
Method
A 7-day cycle, from first call to delivery
A transparent process run in short sprints: you see working prototypes from day one and always know what is being built.
- Day 0Alignment30 minutes to confirm goals, KPIs and data sources. You leave with a clear brief.
- Day 1Mock-upsPrototypes of the main screens in light and dark mode.
- Days 2 to 4BuildData model, DAX measures or Python/R scripts, and visual integration.
- Day 5DeliveryWalk-through of the deliverable, source files handed over and a round of changes.
- Days 6 and 7CloseFinal tweaks and knowledge transfer.
FAQ
Questions about data engineering
What does a data engineering consultant do?
They build the plumbing that moves data from your operational systems into a model ready for analysis: extracting it, cleaning it, joining sources and loading it on a schedule. The result is a reliable foundation that dashboards and models can sit on without manual work.
How often is the data refreshed?
You decide, based on what you need and what your systems allow: daily, weekly or at each month-end close. The processes run on their own and flag any source that fails, so nobody has to check them by hand.
Which tools do you use?
Mainly SQL and PostgreSQL for databases, and Python and R for cleaning and transformation. For visualisation we use Power BI, Tableau or Qlik. If you already have tools in place, we build on them instead of replacing them.
Do you work remotely?
Yes. We work 100 % remotely, and the whole service can be delivered in English: meetings, reports and documentation. We also work in Spanish and French, so teams spread across several countries can each use their own language. The initial 30-minute assessment is always a video call.
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