Introduction

Data engineering is one of the fastest-growing and highest-paying careers in technology-yet most recent graduates and career explorers are unsure what the role actually involves, how it differs from data analysis or data science, and what it takes to land a first position. This course gives you a clear, honest, and structured answer to all three questions, without requiring any prior technical experience.

Module 1: The Data Engineer's Role

In Module 1, you will build a precise understanding of what Data Engineers do day-to-day-the pipelines they build, the data lifecycle they manage, and the teams they collaborate with. You will learn how the DE role compares to adjacent positions (Data Analyst, Data Scientist, DevOps Engineer) and where the responsibilities overlap in real organisations. Every example is grounded in the Microsoft Fabric ecosystem-OneLake, Eventhouse, Data Factory, and the Medallion Architecture-so your mental model reflects how data engineering actually works in 2026.

Module 2: The Skills That Matter

In Module 2, you will map the full technical competency landscape-SQL, Python, Apache Airflow, dbt, Kafka, and cloud platforms-against the end-to-end data lifecycle, so you understand not just what the tools are, but why each one exists and where it fits. You will also explore the non-technical skills that employers now weight as heavily as coding: communicating pipeline logic to non-technical stakeholders, applying structured problem-solving when data quality fails, prioritising competing requests, and staying current as the tool landscape evolves.

Module 3: Building Your Career Path

In Module 3, you will translate everything you have learned into a concrete, personalised action plan. You will examine the three most common entry pathways into data engineering in 2026-graduate entry, career transition, and self-taught-and apply a two-axis prioritisation framework to identify the highest-impact skills to develop first. You will complete the course with a three-phase Career Readiness Plan-a Role Clarity Map, a Skills Inventory, and a 90-Day Personal Learning Roadmap-referenced against Microsoft certifications (DP-900, DP-700) as credentialing anchors. This course is the recommended entry point before MS-CC-132: Real Life Applications of Data Engineer Skills

Syllabus

Differentiate the core responsibilities, daily tasks, and cross-functional collaborations of a Data Engineer compared to other data roles-and articulate the DE's place within a modern technology organisation.
Describe the high-level, end-to-end data lifecycle and identify the foundational tools (SQL, Python, Airflow, cloud platforms, Microsoft Fabric) used at each stage.
Apply non-technical skills-communicating technical decisions to non-technical audiences, structured problem-solving, and prioritisation-to common Data Engineer workplace scenarios.
Evaluate a scaffolded framework of data engineering competencies to build a personalised, prioritised learning roadmap grounded in current 2026 industry entry requirements.
Complete a three-phase Career Readiness Plan: a Role Clarity Map, a Skills Inventory, and a 90-Day Personal Learning Roadmap with Microsoft certification pathway guidance.