While Data Scientists Get the Glory, Data Engineers Get Paid More And Companies Can’t Find Enough of Them
Nigeria’s data explosion has created a critical shortage: companies drowning in data but lacking the engineers to make it usable. While everyone rushes to become data scientists, the real opportunity—better pay, higher demand, fewer qualified competitors is in data engineering. In 16 weeks, you could become one of the rare professionals every tech company desperately needs.
₦500,000
Also available: 4 monthly payments of ₦130,000
Money-back guarantee • No hidden fees • Secure payment
₦350k–₦800k
Earning Potential
5 Real Projects
Portfolio-Ready
Alumni Network
Flutterwave, Andela
Career Support
Lifetime Access
Who This Bootcamp Is For
This program is designed for driven individuals ready to invest in their future
Recent Graduates
STEM or non-STEM graduates struggling to find jobs that match their potential
Underemployed Professionals
Working in roles below your capability and ready for a tech career upgrade
Career Switchers
Professionals from banking, engineering, or other fields seeking tech transition
Parents & Sponsors
Investing in your child's or mentee's future with a high-ROI skill
Requirements Checklist
Basic computer literacy (can use Excel, browse the web)
Access to a laptop and stable internet connection
20-25 hours per week to dedicate to learning
Growth mindset and willingness to be challenged
No prior programming experience required
Requirements Checklist
You're looking for 'get rich quick' shortcuts
You can't commit 20+ hours weekly for 16 weeks
You're not willing to struggle through difficult problems
You expect to learn without putting in the work
You're not ready to invest in your own growth
18-Week Curriculum Overview
In 18 weeks, you’ll master the intersection of scientific computing and software engineering—the skills that make you irreplaceable.
Phase 1
FOUNDATIONS & DATABASE MASTERY
Weeks 1-4
Module 1: Advanced Python for Data Engineering
Object-oriented programming for data pipelines
Working with files, APIs, and data formats (JSON, CSV, Parquet, Avro)
Error handling, logging, and debugging production code
Python virtual environments and dependency management
Real-world application: Build a production-grade Python package for data extraction that handles errors gracefully and logs all operations
Module 2: SQL Mastery & Database Design
Advanced SQL: window functions, CTEs, query optimization
Indexing strategies and performance tuning
Database normalization and schema design
PostgreSQL administration fundamentals
Real-world application: Design and optimize a database handling 1 million+ Nigerian e-commerce transactions with sub-second query times
Phase 2
DATA PIPELINE ENGINEERING
Weeks 5-9
Module 5: ETL/ELT Pipeline Design
Extract, Transform, Load vs. Extract, Load, Transform patterns
Data validation and quality checks
Idempotent pipeline design (handling failures and reruns)
Pipeline orchestration concepts
Real-world application: Build an ETL pipeline extracting data from 3 sources (APIs, databases, files), transforming it, and loading into a data warehouse—handling failures gracefully
Module 6: Apache Airflow for Workflow Orchestration
DAG (Directed Acyclic Graph) design
Task dependencies and scheduling
Monitoring and alerting
Handling pipeline failures and retries
Real-world application: Orchestrate a complex data pipeline with 15+ tasks, dependencies, and scheduled runs—the kind of system running at every major Nigerian tech company
Phase 3
BIG DATA & CLOUD ENGINEERING
Weeks 10-14
Module 10: Introduction to Spark & Distributed Computing
Why Spark for big data processing
RDDs, DataFrames, and Spark SQL
Transformations vs. actions
Optimizing Spark jobs for performance
Real-world application: Process 10GB+ of Nigerian telecom call records using Spark—tasks impossible with Pandas/Python alone
Module 11: Cloud Data Engineering (AWS)
S3 for data lake storage
AWS Glue for ETL
Redshift for data warehousing
Lambda for serverless data processing
IAM, security, and cost optimization
Real-world application: Build a complete cloud data pipeline on AWS—from raw data landing in S3 to queryable warehouse in Redshift
Module 12: Streaming Data & Real-Time Processing
Apache Kafka fundamentals
Stream processing vs. batch processing
Real-time data pipelines
Handling late-arriving and out-of-order data
Real-world application: Build a real-time fraud detection pipeline processing payment transactions as they occur—like systems at Paystack and Flutterwave
Phase 4
PRODUCTION ENGINEERIG & CAREER
Weeks 15-16
Module 15: Monitoring, Logging & Troubleshooting
Prometheus and Grafana for monitoring
Log aggregation and analysis
Debugging production failures
Incident response and post-mortems
Performance optimization at scale
Module 16: Portfolio Development & Technical Interviewing
Documenting engineering projects for non-technical audiences
System design interview preparation
SQL and Python technical assessments
Behavioral interviews for engineering roles
Negotiating senior engineer salaries
Key Technologies
Python
SQL
TensorFlow
Scikit-learn
Git
AWS
Learn from Industry Experts
Get trained by professionals who’ve built real data science solutions for top
African companies
Olumide Shobanke
Lead Data Science Instructor
Former Senior Data Scientist at Flutterwave with 8+ years experience. Built ML systems processing millions of transactions daily. Passionate about training the next generation of African data scientists.
Aisha Aliyu
Lead Data Science Instructor
Tech recruiter turned career coach with 200+ successful placements in Nigerian tech companies. Former talent lead at Andela. Expert in resume optimization and interview preparation.
1:8
Instructor to student ratio for personalized attention
Weekly
1-on-1 check-ins to track your progress
24hr
Response time for questions on Slack
Career Outcomes
Our graduates don’t just learn data science, they launch careers at
top companies.
87%
Employed within 90 days
\u20a63.2M\u2013\u20a65.5M
Average starting salary annually
200+
Alumni network
150-300%
Salary increase for career switchers
Key Technologies
Success Stories
Limited Spots Available
Your Next Career Starts 5 October 2026. Do Not Miss It.
Second cohort now open — 26 spots remaining at this price. Secure yours before enrollment closes.