Data engineer — pipelines and data quality
Move data from its source to the warehouse, automatically and without loss.
- Block code
- DATA-ENG
The format
Three months, two hours a day.
- Certificate programme · 128 h
- Two hours a day
- 250,000 F — the same fee for all sixty
- All in the same format, at the same fee.
The qualification
A CQP under MINEFOP accreditation.
- Leads to the CQP — Certificat de Qualification Professionnelle, which the ministry used to call the AQP.
- Awarded under MINEFOP accreditation.
- The full programme for the same profession remains open to anyone aiming for the complete qualification.
Sessions
A new session opens every quarter.
Next intake: Monday 5 October, with enrolment open until 9 October. The following one starts on 4 January 2027.
- First-quarter intake — enrolment closes on 9 October5 Oct.
- Second-quarter intake — 20274 Jan.
Objectives
What you will be able to do.
Write advanced SQL queries: joins, window functions, common table expressions
Read an execution plan and fix a costly query
Extract and transform data with Python and pandas
Process a volume of data that does not fit in memory
Build a pipeline of dependent tasks, scheduled and re-runnable
Model a star-schema warehouse and keep the history of a dimension
Set up data quality tests and maintain a data catalogue
Programme
Four modules.
Module 0132 h Module 0232 h Python for data: extraction and transformation
Module 0332 h Building and scheduling a pipeline
Module 0432 h Warehouse, modelling and quality control
Who it is for
Who it is aimed at.
- Learners comfortable with spreadsheets and logic. Some SQL or programming helps but is not required: the block covers both from the basics that matter.
Afterwards
Where it leads.
- Junior data engineer, pipeline developer, data warehouse administrator. Natural next step towards business intelligence (Business intelligence and steering by indicators) or machine learning (Applied machine learning).
Method
How it works.
Systematic alternation between short theory sessions and practical work, with most of the time spent on practice
Progression from simple to complex: each module builds on what was learnt in the previous one
Realistic role-plays drawn from Cameroonian business cases
Considered use of generative AI as a working tool, in line with the common core
Regular formative assessments and a final integrative project drawing on all four modules
Assessment
How you are assessed.
Formative assessment at the end of each module (graded role-play and practical exercises)
Practical work assessed against competency sheets
Final integrative project, presented and defended before the trainer
One graded assignment per module in the online course space
Tools
What you work with.
PostgreSQL — advanced SQL and demonstration warehouse
Python, pandas and a columnar format (Parquet)
A pipeline scheduler (Airflow or a lightweight equivalent)
dbt or equivalent for transformations and tests
An SQL client and an interactive notebook
Keep exploring
In the same field.
Join the next session
Ready to get started?
Sessions open every quarter. Apply now or request the detailed brochure — our advisers will get back to you.