---
url: "https://xcademia.com/courses/aws-data-engineer-associate-training"
title: " AWS Data Engineer Associate Training"
description: " Learn AWS data engineering in this 4-day mentor-led course aligned to DEA-C01. Build pipelines, ETL workflows, and secure data solutions."
publishedAt: "2026-03-20T10:06:41.522363+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "CLD-0095"
level: Professional
duration_days: "4"
track: AWS Tracks
category: "Cloud & DevOps"
credential_tier: tier1
price_gbp: "2199"
---

#  AWS Data Engineer Associate Training

> Build data engineering skills on AWS aligned with the Data Engineer Associate (DEA-C01) certification objectives. Learn through mentor-led sessions and practical scenarios covering ingestion, transformation, orchestration, and storage.

## Overview

This AWS Data Engineer Associate Training programme is designed for professionals who want to build, manage, and optimise data pipelines on AWS. Aligned with the DEA-C01 certification objectives, the course provides a structured approach to modern data engineering practices in cloud environments.

Through mentor-led sessions and practical scenarios, learners will work with AWS services to ingest, transform, and store data efficiently. The course covers data lakes, ETL pipelines, orchestration workflows, and data security, focusing on real-world use cases and scalable architecture patterns.

By the end of the programme, participants will be able to design and manage end-to-end data pipelines, ensure data quality and governance, and implement secure and efficient data processing solutions on AWS. This course supports professionals in data engineering, analytics, and cloud data roles.

## Prerequisites

- Basic understanding of data concepts
- Familiarity with AWS services
- Knowledge of databases or SQL

## What you will learn

- Design scalable AWS data pipelines
- Implement data ingestion and storage solutions
- Analyse data processing performance
- Evaluate data security and governance controls
- Manage orchestration and workflow execution
- Communicate data architecture decisions

## Skills you will gain

- Data pipeline design
- AWS data services usage
- ETL workflow development
- Data storage optimisation
- Pipeline orchestration techniques
- Data security practices

## Career progression

- Data Engineer
- Cloud Data Engineer
- Analytics Engineer
- Data Platform Engineer
- ETL Developer

## Curriculum

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

## Exam & certification

This programme is aligned with the official exam objectives. Exam registration and certification are managed directly by the awarding body.

## 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

**Is this course aligned with AWS Data Engineer Associate certification?**

Yes, it is aligned with the DEA-C01 exam objectives.



**Do I need prior AWS experience?**

Yes, basic familiarity with AWS services is recommended.



**What roles can I pursue after this course?**

Roles include Data Engineer, Analytics Engineer, and Cloud Data Engineer.



**Are hands-on labs included?**

Yes, practical labs are included to reinforce learning.



**Does this course need an exam?**

No, the exam is optional and can be taken separately.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | CLD-0095 |
| Duration | 4 days |
| Level | Professional |
| Track | AWS Tracks |
| Category | Cloud & DevOps |
| Credential tier | tier1 |
| Price (GBP) | £2199 |

---

## About this content

This Markdown course profile is the citation-grade twin of [ AWS Data Engineer Associate Training](https://xcademia.com/courses/aws-data-engineer-associate-training). 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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