Building the Modern Data Stack

The backbone of the $300B data economy. Master cloud infrastructure, data pipelines, and production-ready engineering in this comprehensive 12-week programme.

12 Weeks

Intensive hands-on training

£1000

Total programme fee

5+ Tools

Industry-standard platforms

What You Will Learn

This intensive programme is designed to equip you with production-grade data engineering skills, enabling you to build scalable data infrastructure that powers modern organizations.

🗄️

Master the Data Engineering Lifecycle

Understand ETL/ELT paradigms, data modeling architectures, and the complete data engineering workflow from ingestion to consumption.

☁️

Build Cloud Data Infrastructure

Deploy data lakes and warehouses on AWS/GCP, implement infrastructure as code with Terraform, and optimize cloud data storage.

🔄

Orchestrate Data Pipelines

Create production-ready data workflows using Apache Airflow, automate ETL processes, and manage complex data dependencies.

⚡

Process Data at Scale

Handle big data using Apache Spark and PySpark for distributed computing and batch processing of multi-GB datasets.

🛠️

Transform Data with dbt

Implement modern analytics engineering practices, build data transformation models, and ensure data quality at scale.

🚀

Deploy Production Systems

Build a complete end-to-end data platform as your capstone project and graduate with a production-ready portfolio.

Who Is This Programme For?

This programme is designed for technical professionals ready to advance into data engineering roles.

Software Engineers

Developers with Python or other programming experience who want to specialize in data infrastructure and transition into high-demand data engineering roles.

Data Analysts

Analytics professionals with SQL skills who want to move upstream into building the data pipelines and infrastructure that power analytics.

Technical STEM Graduates

Recent graduates with strong technical backgrounds in computer science, mathematics, or engineering who want to launch their career in data engineering.

Programme Highlights

🎓

Industry Expertise

Learn from data engineers with production experience at scale

⚙️

Hands-On Engineering

60% hands-on-keyboard time building real data infrastructure

🏗️

Production Portfolio

Deploy live data pipelines and infrastructure projects

👥

Personal Mentorship

One-on-one technical mentorship and architecture reviews

📄

CV Preparation

Professional CV reviews optimized for data engineering roles

⏰

Flexible Schedule

2 sessions per week (3 hours each) for working professionals

🐛

Office Hours

Debugging marathons and technical problem-solving sessions

🚀

Career Ready

Graduate with deployable projects and technical interview prep

Curriculum Overview

A comprehensive 12-week journey from fundamentals to production-ready data engineering.

Phase 1: The Foundations (Weeks 1-5)

Establishing the core skills of the data engineering lifecycle.

WEEK 01

Introduction to the Data Engineering Lifecycle

Topics

  • Role of a Data Engineer vs. Data Scientist
  • Structured vs. Unstructured data
  • The ETL/ELT paradigm

Activity

  • Set up development environment (Docker, VS Code, Git)
WEEK 02

SQL Foundations for Data Engineers

Topics

  • SQL Basics: SELECT, WHERE, ORDER BY, LIMIT
  • Data Types and Type Casting
  • Filtering with AND, OR, IN, BETWEEN, LIKE
  • Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
  • GROUP BY and HAVING clauses

Activity

  • Write queries to explore and analyze a sample e-commerce database
WEEK 03

Advanced SQL for Data Engineering

Topics

  • JOINs: INNER, LEFT, RIGHT, FULL OUTER, CROSS
  • Subqueries and Nested Queries
  • Common Table Expressions (CTEs) and WITH clauses
  • Window Functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
  • Query Optimization and Performance Tuning
  • Indexes and Execution Plans

Activity

  • Design and optimize complex analytical queries for a retail dataset
WEEK 04

Python for Data Automation

Topics

  • Virtual environments; Pandas for transformation
  • Interacting with APIs

Activity

  • Build a script to extract live weather or finance data from an API
WEEK 05

Data Modeling & Architecture

Topics

  • Star vs. Snowflake schemas
  • Normalization vs. Denormalization
  • Medallion Architecture (Bronze/Silver/Gold)

Activity

  • Model a "Star Schema" for a sample Airbnb dataset
🏆 Milestone: Your first Relational Schema

Phase 2: Building the Infrastructure (Weeks 6-9)

Moving data into the cloud and orchestrating workflows.

WEEK 06

Cloud Storage & Data Lakes

Topics

  • Intro to AWS S3 / Google Cloud Storage
  • Infrastructure as Code (Terraform)

Activity

  • Use Terraform to provision a cloud storage bucket and upload raw data
WEEK 07

Modern Data Warehousing

Topics

  • BigQuery/Snowflake internals
  • Partitioning and Clustering

Activity

  • Load data from a Data Lake into a Warehouse and optimize query performance
WEEK 08

Workflow Orchestration

Topics

  • Introduction to DAGs (Directed Acyclic Graphs)
  • Airflow vs. Dagster vs. Mage
  • Orchestration: Apache Airflow DAGs

Activity

  • Create an Airflow DAG to automate the Week 3 API script
WEEK 09

Analytics Engineering with dbt

Topics

  • Transforming data inside the warehouse
  • The dbt Revolution: SQL Transformations
  • Data Quality & Metric Stores
  • Version control for SQL

Activity

  • Use dbt to build a production-ready view of your Star Schema
🚀 Milestone: An automated ETL pipeline in the Cloud

Phase 3: Scaling & Production (Weeks 10-12)

Advanced processing and professional deployment.

WEEK 10

Batch Processing at Scale (Spark)

Topics

  • Distributed computing; PySpark
  • Handling "Big Data"

Activity

  • Process a multi-GB dataset using a Spark cluster
WEEK 11-12

Capstone & Professional Deployment

  • Real-time data integration
  • Production-ready Capstone project completion
  • Portfolio presentation and review
🎯 Milestone: Production-ready data platform portfolio

Ready to Build the Modern Data Stack?

Join our next cohort and become a production-ready data engineer

£1000
  • Duration 12 Weeks
  • Schedule 2x per week (3 hours)
  • Format Live Online Sessions
  • Class Size Limited to 15 students
  • Career Support Mentorship & CV Prep Included
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