Certificate programme
Data Engineering
Behind every dashboard that refreshes itself and every model that receives fresh data every morning, there is a data engineer. It is the least visible profession in the chain, yet the one without which nothing works over the long term.
- Duration
- 5 weeks, 10 sessions, 50 hours
- Format
- In person or online
- Credential
- Training certificate
Tuition195,000 XOF
By the end of the programme, you will be able to
- Design an organisation's data architecture, from the sources to the reporting layer
- Work on the Linux command line and version your work with Git
- Write an engineer's SQL: window functions, indexes, reading execution plans, materialised views
- Model a star-schema data warehouse and handle the historisation of dimensions
- Build ETL and ELT pipelines that are robust and can be re-run safely
- Extract data from an API, validate it and load it into a warehouse
- Orchestrate scheduled jobs with Airflow, with dependencies and recovery on failure
- Put automatic data quality checks and alerts in place
- Deploy a data pipeline to the cloud at a controlled cost, with Docker Compose
- Deliver a complete platform: tested, orchestrated and monitored
Who it is for
Target audience
The certificate is for technical profiles who want to build the infrastructure rather than consume it: developers, database administrators, IT professionals, data analysts who want to move up, computer science or software engineering graduates, information system managers.
Prerequisites
Four requirements:
- Hold at least a three-year degree (BAC+3) in computer science, statistics, mathematics or engineering, or have equivalent professional experience
- Master SQL up to joins and aggregations; the Waaldé Impact Data Analyst certificate or SQL course meets this requirement
- Master the basics of Python: data structures, functions, file and error handling
- Have a laptop with at least 8 GB of RAM, preferably 16 GB, and 50 GB of free disk space
A fifteen-minute technical interview is held before any enrolment. This is the most technical course in the catalogue: a candidate who does not code at all will not find their place here and will be directed to the Data Analyst certificate.
Teaching method
The certificate runs over five weeks, with two sessions a week, on Saturday and Sunday from 8.30 a.m. to 1.30 p.m.: ten five-hour sessions, 50 supervised hours in all. Personal work comes to two or three hours a week, and three to four during the project week.
The format is the flipped classroom. The concepts are covered beforehand, in pre-recorded video capsules given to every participant and watched remotely whenever they choose, one to two hours before each session. The session itself is given over entirely to practice: you build, you break, you repair. Each session also has a "going further" section covered in the course material.
Sessions can be followed in person in Ouagadougou or online and live, as each participant prefers, at the same price. The cohort is limited to twelve for this course, because the individual technical supervision is particularly demanding. A fifteen-minute technical interview comes before any enrolment.
Every participant leaves with:
- The complete course material in PDF, module by module
- The pre-recorded video capsules for every module
- A reference code repository holding every pipeline built in class
- A project template ready to clone: Docker Compose, Airflow, dbt, warehouse, tests, monitoring
- Reusable Airflow DAG and dbt project templates
- A cheat sheet for SQL optimisation, Linux and Git
- An architecture design grid and a cost estimation grid
- A sample runbook and a pre-production checklist
- A guide to preparing for engineering interviews
Every tool taught is free and open source, and the exercises are designed to stay within cloud providers' free tiers.
Detailed program
10 sessions · 50 h of teaching
Week 1 — The profession, the environment and an engineer's SQL
2 sessions · 10 h- Sketch on paper the target architecture of a partner organisation and defend it in front of the group
- Navigate, manage permissions and chain commands and redirections under Linux
- Schedule a task with cron and move a secret out of the code into an environment variable
- Create a Git repository, work on branches and resolve a merge conflict
- Publish the project on GitHub and have it reviewed in a pull request
- Exercise: set up your complete workstation and publish a first version-controlled project
- Going further, in the course material: warehouse, data lake and hybrid architecture; batch versus stream processing; grep, cut, sort and awk; Python virtual environments
- Remotely, before the session (about 1 h 15): video capsules on window functions, indexes and their cost, reading an execution plan, materialised views
- Compute a rank, a lag and a running total on a transactions table with window functions
- Create tables with keys and constraints, then measure what an index does to a read and to a write
- Read the execution plan of five slow queries and name the cause of each slowdown
- Create a materialised view for a recurring query and measure the gain
- Exercise: cut the response time of five queries on a supplied database by a factor of ten
- Going further, in the course material: data types and their effect on volume; transactions and consistency; partitioning a large table; backup, restore and user permissions; stored procedures and functions
Week 2 — Warehouse modelling and transformation
2 sessions · 10 h- Define the grain of the fact table of a project monitoring system, then derive its dimensions
- Build the date dimension and organise the warehouse in three layers: raw, intermediate, exposed
- Write an SQL transformation that can be re-run without creating duplicates
- Historise a slowly changing dimension and check the result after a change at the source
- Exercise: model and load the complete warehouse of a project monitoring system
- Going further, in the course material: normalisation and its forms, and why analytics relaxes it; the snowflake schema; additive, semi-additive and non-additive facts; surrogate keys; capturing changes and deletions at the source
- Query an API with authentication and page through every screen of results
- Handle network errors with spaced retries and timeouts
- Validate every incoming record and set aside rejected rows, with the reason for each rejection
- Write automated tests on the pipeline and run them before every load
- Exercise: build a complete ingestion pipeline, from the API through to the warehouse
- Going further, in the course material: scraping a web page when no API exists, and the legal limits; logging properly — levels, format, destination; reading and writing large files, including the Parquet format
Week 3 — Orchestration and quality
2 sessions · 10 h- Install Airflow locally and open its monitoring interface
- Write a three-task graph with dependencies and a daily schedule
- Configure retries, delays and a notification on failure
- Diagnose a failed run from the interface and the logs
- Exercise: orchestrate a complete three-step pipeline, with dependencies and alerts
- Going further, in the course material: operators for Python, SQL and shell; passing data between tasks; backfilling missed periods in detail; Prefect and Dagster, and when they suit better
- Write explicit checks on a table: uniqueness, not-null, accepted values, freshness
- Decide for each check whether it blocks the pipeline or merely raises an alert
- Log runs and build a dashboard of duration, volume and failure rate with an open-source tool
- Deliberately break your own pipeline twice, then detect and repair it
- Exercise: instrument your pipeline with automatic checks and a lateness alert
- Going further, in the course material: Great Expectations and dbt's built-in tests; referential integrity checks; a data contract between producer and consumer; runbook and pre-production checklist; pseudonymisation and retention periods
Week 4 — Modern warehouses and transformation with dbt
2 sessions · 10 h- Load a large dataset into DuckDB locally, then compare it with a partitioned table in BigQuery
- Optimise an analytical query through partitioning, clustering and column selection, then compare the scan cost
- Initialise a dbt project, create models and link them to one another
- Declare sources, add generic tests and generate the documentation
- Exercise: rebuild week 2's warehouse with dbt, complete with tests and documentation
- Going further, in the course material: the Parquet format and object storage as a raw layer; macros and factoring out repetitive code; snapshots for automatic historisation; development and production environments
- Create a virtual machine on a cloud provider's free tier and deploy a pipeline on it
- Estimate the monthly cost of a platform and spot the items that most often drift
- Deploy Airflow, a database and a warehouse together with Docker Compose
- Exercise: scope your project — case, sources, layers, checks and cost — then submit the architecture document for approval
- Going further, in the course material: serverless functions for event-driven processing; managed databases and the trade-off between running it yourself and paying for a service; free tiers for an organisation on a tight budget; infrastructure as code
Week 5 — The platform project, defence and certificates
2 sessions · 10 h- Set up ingestion from the project's sources and the dbt transformations with their tests
- Orchestrate the jobs under Airflow, with dependencies and alerts
- Connect the monitoring and the data quality dashboard
- Version the project, document it and test it at every change
- Exercise: run the platform end to end after a one-to-one session with the trainer
- Going further, in the course material: connecting a reporting tool to the exposed layer; peer code review between participants; runbook templates
- Present your platform in ten minutes: architecture, technical choices, costs and limits
- Answer five minutes of questions from the group and the trainer
- Trigger the pipeline live, cause a failure and show the alert during the presentation
- Publish the project and build your technical portfolio
- Exercise: defend your platform in front of the group, then receive your certificate
- Going further, in the course material: the vendor certifications that complement this course; the guide to preparing for engineering interviews
Assessment and certification
The Data Engineer certificate is awarded on an admission average out of 20, made up of three components:
- The certification exam (50%) — 25 questions drawn at random from a bank of more than eighty, in 90 minutes. Two attempts are allowed, seven days apart, and the better one counts.
- The platform project (30%) — defended in the tenth session.
- Attendance (20%) — recorded session by session. An absence announced at least three days in advance counts as half; an unannounced absence does not count.
The admission average is 12 out of 20. A mark below 8 out of 20 in the exam is eliminatory: neither the project nor attendance can make up for it. As soon as the average is reached, the certificate is issued automatically and becomes available in the participant's account.
The certificate is issued by Waaldé Impact, signed by the trainer, and states the course title, the 50 hours, the period, the list of modules completed and the subject of the platform project presented. Each certificate carries a unique number and a verification code.
Participants who do not reach the average receive a certificate of attendance. They may retake the exam and present their platform project with the following cohort, at no extra cost.
Upcoming sessions
Next dates being prepared
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Frequently asked questions
195,000 XOF per participant. Training for a team or an organisation is quoted on request.
5 weeks, 10 sessions, 50 hours.
Yes: you can attend onsite or online, as you prefer.
Four requirements: hold at least a three-year degree (BAC+3) in computer science, statistics, mathematics or engineering, or have equivalent professional experience ; master SQL up to joins and aggregations; the Waaldé Impact Data Analyst certificate or SQL course meets this requirement ; master the basics of Python: data structures, functions, file and error handling ; have a laptop with at least 8 GB of RAM, preferably 16 GB, and 50 GB of free disk space ; a fifteen-minute technical interview is held before any enrolment. This is the most technical course in the catalogue: a candidate who does not code at all will not find their place here and will be directed to the Data Analyst…
Yes: participants who meet the completion requirements receive a Waaldé Impact certificate with a unique number that employers can verify online.
Fill in the online enrolment request: no payment is taken on the site. The firm reviews your application, then contacts you to confirm your place and the payment terms. No session is scheduled yet: leave your email on this page to be told when registration opens.
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