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Data Engineering- Professional

The course equips learners to learn core data engineering skills, including database fundamentals, ETL concepts, data modeling, and basic cloud tooling to support enterprise data operations.
  • Length
2 Months
  • Capstone
1 Months
  • Certificate
Yes
  • Level
Professional
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About the course

Data Engineering Professional is a practical, hands-on program designed to equip learners with the technical skills needed to build, manage, and optimize modern data infrastructure. The course introduces learners to Python programming, SQL, database management, data pipelines, ETL processes, cloud technologies, and big data fundamentals. Learners gain practical experience working with structured and unstructured data, automating data workflows, and building scalable systems that support analytics and business intelligence operations. Through real-world projects and industry-standard tools, students develop the expertise required for practical data engineering and data infrastructure roles.

Course Curriculum

MODULE 1: INTRODUCTION TO DATA ENGINEERING
  • Who is a Data Engineer?
  • Data Engineering vs Data Science vs Machine Learning
  • Modern Data Stack
  • Data Engineering Career Paths
  • Key Tools & Technologies
  • Data Lifecycle & Data Architecture
  • Databases vs Data Warehouses vs Data Lakes

    Hands-On

  • Set up environment: Python, VS Code, Git, GitHub

  • Explore sample datasets

  • Create your first data pipeline workflow manually

  • Python basics for data engineering
  • Working with files: CSV, JSON, XML, Parquet
  • Error handling, logging, virtual environments
  • Data manipulation with Pandas
  • Introduction to OOP
  • Writing production-level Python scripts
  • Understanding APIs (REST, SOAP)

    Hands-On

    • Build ETL scripts using Python

    • Consume public APIs and save data

    • Clean large datasets with Pandas

    • Create scheduled script
  •  
  • Relational Database Concepts
  • SQL
  • Indexing & Query Optimization
  • PostgreSQL / MySQL
  • NoSQL Databases: MongoDB, Cassandra
  • Data Modeling for OLTP & OLAP
  • Star & Snowflake Schema

Hands-On

  • Design a data model for an E-commerce system

  • Optimize slow SQL queries

  • Create OLAP star schema

  • Build a mini data warehouse in PostgreSQL
  • Data Warehouse Architecture
  • ETL vs ELT frameworks
  • Dimensional Modeling
  • Slowly Changing Dimensions
  • Fact & Dimension Tables
  • Partitioning & Clustering 
  • Cloud Data Warehouse: Snowflake / BigQuery / Redshift

Hands-On

  • Build a full ETL pipeline using Python

  • Load data into a cloud data warehouse

  • Build SCD Type 1 & 2 transformations
  •  
  • AWS IAM
  • S3 (object storage)
  • EC2
  • RDS
  • Lambda Functions
  • Glue
  • Athena
  • Cloud security basics

Hands-On

  • Build a pipeline that extracts data → stores on S3 → loads to Redshift

  • Trigger ETL with AWS Lambda

  • Create automated Glue jobs
  • Introduction to Big Data
  • Hadoop Ecosystem
  • HDFS
  • MapReduce
  • Spark Architecture
  • PySpark
  • Spark SQL
  • Spark Optimization

Hands-On

  • Process 5–10GB datasets with Spark

  • Build and optimize Spark ETL pipelines

  • Run Spark jobs on AWS EMR
  •  

Course Structure

  • Saturday Live Classes for 2 months
  • 1-Month Capstone
  • CV Review
  • LinkedIn Optimization
  • Interview Preparation
  • Post-Training Support
  • Mentorship Sessions
  • Career Pathway Guidance

Opeyemi Agboola

Data Engineering Facilitator

Opeyemi  a passionate and results-driven Data Engineering professional with strong expertise in building scalable data solutions and transforming raw data into valuable business insights. With experience in data pipelines, database management, cloud technologies, and big data systems, he is committed to helping learners develop practical, industry-relevant skills