---
url: "https://xcademia.com/courses/data-engineer"
title: Data Engineer
description: "Become a Data Engineer in 5 days. Master batch and streaming pipelines, data modelling, quality checks, and modern lakehouse architectures. Mentor-led coaching."
publishedAt: "2026-03-17T06:09:26.796636+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "AID-0033"
level: Professional
duration_days: "5"
track: "Data Engineering & Warehousing"
category: "AI, Data & Analytics"
credential_tier: tier1
price_gbp: "2499"
---

# Data Engineer

> Build scalable data pipelines using batch and streaming patterns for modern analytics platforms. Learn mentor-led techniques through practical scenarios covering modelling, quality, and lakehouse design.

## Overview

Modern organisations rely on robust data platforms to power analytics, AI, and real-time decision-making. Data Engineers play a critical role in building scalable pipelines that transform raw data into reliable, high-quality datasets.

This programme provides a comprehensive foundation in data engineering, covering batch and streaming processing, data modelling techniques, pipeline orchestration, and data quality practices. Participants also explore modern warehouse and lakehouse architectures used in enterprise environments.

Through hands-on labs, learners design and implement end-to-end data pipelines, ensuring performance, reliability, and scalability across data platforms.

## Prerequisites

- Basic programming knowledge (Python or SQL recommended)
- Understanding of databases and data concepts
- Familiarity with cloud platforms helpful

## What you will learn

- Design and build batch and streaming data pipelines
- Apply data modelling techniques for analytics systems
- Implement data quality checks and validation
- Work with warehouse and lakehouse architectures
- Orchestrate and automate data workflows
- Build scalable and reliable data platforms

## Skills you will gain

- Batch and streaming data processing
- Data modelling and schema design
- ETL/ELT pipeline development
- Data warehousing and lakehouse architecture
- Data quality and validation
- Pipeline orchestration and automation
- Scalable data platform design

## Career progression

- Data Engineer
- Analytics Engineer
- Big Data Engineer
- ETL Developer
- Data Platform Engineer

## Curriculum

1. **Module 1**
2. **Module 2**
3. **Module 3**
4. **Module 4**

## Exam & certification

## Delivery options

- **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more.
- **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning.
- **Blended** — Combine online and in-person learning for maximum flexibility and impact.

## Frequently asked questions

**1. What does a Data Engineer do?**

A Data Engineer builds and manages data pipelines that transform raw data into usable datasets for analytics and AI.



**2. Is this course hands-on?**

Yes. Participants build real-world pipelines and apply data engineering concepts in practical labs.



**3. What is the difference between batch and streaming?**

Batch processes data in chunks at intervals, while streaming processes data in real-time.



**4. What is a lakehouse architecture?**

A lakehouse combines the scalability of data lakes with the structure and performance of data warehouses.



**5. What roles benefit from this training?**

Data engineers, ETL developers, analytics engineers, and professionals working with data platforms.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0033 |
| Duration | 5 days |
| Level | Professional |
| Track | Data Engineering & Warehousing |
| Category | AI, Data & Analytics |
| Credential tier | tier1 |
| Price (GBP) | £2499 |

---

## About this content

This Markdown course profile is the citation-grade twin of [Data Engineer](https://xcademia.com/courses/data-engineer). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite.

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