Our Data Engineering Course equips you with the skills to design, build, and maintain real-world data pipelines and analytics infrastructure.
Over 6 months, you’ll learn directly from experienced data engineers, gain hands-on project experience, and graduate ready to work in data engineering roles or support data-driven products and startups with robust, scalable data systems.
Data engineering is the field focused on designing, developing, deploying, and maintaining the data systems and pipelines that power real-world analytics, machine learning, and business intelligence applications.
| Beginner (2026) – Jan 12 to Mar 12 |
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Break – 1 week (with development assignments and one-on-one with instructors sessions) | N/A |
| Intermediate (2026) – Mar 23 to May 22 |
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| Advanced (2026) – May 25 to July 20 |
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| High Demand | AI engineers are among the most sought-after professionals worldwide. |
| Future-Proof Career | AI is driving the next wave of technological advancement. |
| Lucrative Opportunities | AI skills open doors to high-paying global roles. |
| Real Impact | Build solutions that solve real-world problems. |
Inspiring Story: $250M Offer to 24-Year-Old AI Prodigy
The power of honing elite skills, and being recognized for them, is best illustrated by Matt Deitke:
Not necessarily.
Basic programming experience is helpful, but it isn’t a strict requirement. We start from the fundamentals and gradually build up your skills in Python, SQL, and data tools—even if you’ve never coded before. What matters most is your commitment to learning.
By the time you complete the program, you’ll confidently write production-level code for data pipelines, automation scripts, and analytics workflows.
Hybrid & Virtual Combined
Yes — Data Engineers are among the most in-demand tech professionals globally. Every organization that relies on analytics, AI, or large-scale data processing needs skilled data engineers to build and maintain their data infrastructure.
Career Paths You Can Pursue
With the skills gained in this program, you can confidently apply for roles such as:
We Support You Until You Get the Job
We provide ongoing support, including:
1. Problem Definition
Understanding business or organizational data needs and determining the most efficient way to collect, store, and move data.
2. Data Pipeline Development
Designing, building, and automating ETL/ELT pipelines that extract data from multiple sources, transform it, and load it into databases, warehouses, or data lakes.
3. Data Modeling & Architecture
Structuring data into clean, organized formats—designing schemas, tables, and architectures that support analytics, machine learning, and business applications.
4. Infrastructure Setup & Management
Setting up and managing cloud or on-premise data environments (e.g., AWS, GCP, Azure). Ensuring systems are scalable, secure, and high-performing.
5. Real-Time & Batch Processing
Implementing systems for both streaming data (Kafka, Kinesis, Pub/Sub) and scheduled batch jobs (Airflow, Prefect).
6. Optimization & Performance Tuning
Improving pipeline efficiency, reducing processing time, optimizing queries, lowering cloud costs, and ensuring high data quality.
7. Data Governance & Compliance
Ensuring data privacy, security, lineage, and compliance with standards such as GDPR, HIPAA, or local data regulations.
8. Collaboration with Teams
Working with data scientists, analysts, software engineers, and product teams to ensure data is readily available, reliable, and usable across the organization.
Banking: An AI engineer builds fraud detection models that automatically flag suspicious transactions.
Healthcare: An AI engineer develops systems that analyze X-rays, MRIs, or scans to assist doctors in diagnosis.
Retail & E-commerce: An AI engineer designs recommendation systems that personalize product suggestions for customers.
By the end of the program, you’ll be able to design, build, and manage data systems used in real companies. You will gain skills in:
Yes. You’ll receive a recognized and verifiable certificate of completion that can be shared with employers, added to LinkedIn, or used in startup pitches.
According to PayScale, the average base salary for a Data Engineer in Kenya is about KES 1,210,095/year.
For a company like Safaricom, reports show a Data Engineer might earn roughly KES 2.4 million/year base, with additional pay (bonuses, profit share, etc.).
1. Data is the foundation of every AI product
AI systems are only as good as the data behind them. With Data Engineering skills, you’ll know how to:
Collect and clean data
Build pipelines that keep your data updated
Store and organize data efficiently
Ensure data quality, privacy, and security
This is exactly what every AI startup needs before building models.
2. You can build your own AI-powered products
With strong data engineering skills, you can support:
AI chatbots
Recommendation engines
Predictive analytics tools
Fraud detection systems
Automation tools
Industry-specific AI solutions
You’ll be able to build the data backend that powers all these applications.
3. You can offer freelance or consulting services
Many companies need help with:
Setting up databases
Building dashboards
Automating data workflows
Cleaning and migrating data
Integrating AI or analytics
You can start an agency, consultancy, or remote freelancing business.
4. You can combine Data Engineering + AI Engineering
This combination makes you extremely powerful:
Data Engineering = prepares the data
AI Engineering = builds and deploys the AI
You can deliver end-to-end AI solutions, making your startup more competitive.
5. You will develop problem-solving and product-building skills
During the course, you’ll build:
Real projects
Data pipelines
Cloud infrastructure
Production-ready systems
These are essential skills for launching any tech startup.
Hands-on learning with real-world datasets, pipelines, and projects.
African context — including case studies of local data-driven success stories like M-KOPA, Flutterwave, Branch, Twiga Foods, Kobo360, and Apollo Agriculture.
Mentorship from data engineering professionals and tech startup founders with experience in building scalable data systems in Africa.
