Data engineering

What is it?

Data engineering means designing the pipelines that collect, transform and centralise your raw data. It is the essential foundation before any AI integration: without clean, accessible data, LLMs and ML models cannot deliver reliable results.

How it works

1. 1

   Source audit

   Mapping your data sources (SQL databases, APIs, files, SaaS tools) and identifying quality issues, duplicates and silos.

2. 2

   Pipeline architecture

   Designing the target architecture: stack selection (dbt, Airflow, Prefect), data model, ingestion and transformation strategies.

3. 3

   Development and testing

   Developing dbt transformations, Airflow DAGs and connectors. Unit tests and regression tests on the data.

4. 4

   Production monitoring

   Deployment with pipeline error alerts, data freshness tests and technical documentation for your team.

5. 5

   Maintenance

   Pipeline onboarding support, adjustments as data sources evolve and occasional support for new integrations.

What it covers

• Reliable data collection and transformation pipelines, from zero to production
• Modern tooling: Python, dbt, Airflow
• From connecting new sources to preparing datasets for model training

Related projects

• Email Processing Pipeline - Pretto

Frequently asked questions

Why structure my data before integrating AI?

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An LLM or ML model does not improve bad data quality. If your data is fragmented across tools, uncleaned or lacks a common definition, the model will learn the inconsistencies. Data engineering upstream ensures AI works on a reliable foundation.

What tools do you use?

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Python for collection and transformation, dbt for SQL transformations and data model documentation, Airflow or Prefect for orchestration, and PostgreSQL or BigQuery depending on context. The stack is chosen based on your existing setup and constraints.

Can we start with very fragmented data?

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Yes, that is precisely the most common case. The first step is always an audit to assess existing quality and structure. We start with the most critical sources for your priority use case, then extend gradually.

Is the ETL pipeline maintained after delivery?

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Delivery includes full technical documentation and a knowledge transfer so your team can evolve the pipelines. Occasional maintenance engagements are available depending on your needs.