IT outsourcing
Data engineering
Pipelines that look after themselves 95 percent of the time and tell you when they do not.
In summary: building and maintaining data pipelines, integration between systems and data preparation for analytics and artificial intelligence, with quality controls and observability designed in.
The difference between a data pipeline that works and one that causes trouble is not the technology, it is whether it tells you when it fails. A silent pipeline delivering incomplete data does more damage than one that stops.
So observability and data quality controls are part of the initial design rather than a later phase that never arrives.
What is included
According to architecture and the sources involved.
- Design and construction of data pipelines
- Integration between heterogeneous systems and sources
- Modelling and preparing data for analytics
- Preparing datasets for model training and evaluation
- Data quality controls and anomaly alerting
- Documentation of data lineage and applied transformations
What is not included
Worth separating engineering from analysis.
- Business analysis and interpretation of the data, which belongs to your teams
- Decisions on which metrics define your business
- Acquiring third-party data on our own account
- Model training, which sits in the artificial intelligence pillar
Where it is delivered from
From Poland where data must stay inside the European Economic Area, which is common in this service because pipelines usually touch personal data at some point.
From Uzbekistan for platform work on test or already anonymised data, measured by delivery.
Processing and minimisation
In data engineering the decision with the most consequences is which fields enter the pipeline. An identifying field not needed for the use case multiplies risk, obligations and the cost of an eventual deletion, while adding nothing.
So design starts from a field inventory and its justification, not from the tool. It is also the right moment to decide whether the case allows pseudonymisation.
If the pipeline processes personal data from outside the European Economic Area, standard contractual clauses apply and, depending on sensitivity, a transfer impact assessment.
Frequently asked questions
Do you work with our current architecture?
Yes. The first phase documents what exists, including the undocumented integrations that almost always turn up, and from there we decide what is worth rebuilding and what is not.
What happens when a source changes its format?
It is the most common production failure. The pipeline should detect it and alert rather than silently processing incorrect data, and that is designed in from the start.
Do you prepare data for artificial intelligence?
Yes, including training and evaluation dataset preparation. Labelling in Spanish and co-official languages sits in the artificial intelligence pillar.
Do you document data lineage?
Yes, and it is worth requiring of any provider. Without documented lineage, answering a request about a specific data point becomes an investigation.
Need to get your data in order?
Tell us the sources, the destination and what decisions you expect to make with the data.