AWS Certifications for Data Engineers in 2025: A Complete Guide

Most Relevant Certifications for Data Engineers

Now that you know what AWS certifications are, let’s look at the ones that matter most to data engineers. Each one focuses on the skills you need to design, build, and manage cloud-based data systems, and I’ll help you choose the right path based on your experience.

AWS data engineer certifications. Image by Author. 

AWS Certified Data Engineer (Associate level)

The AWS Certified Data Engineer certification is for people who want to build and manage data pipelines on AWS. It focuses on moving data into AWS, processing it using ETL workflows, and storing it in the right services and formats for analysis and long-term use.

You'll work with tools like AWS Glue and SQL to clean, transform, and move data. You'll also learn to store that data in Amazon S3, choose between different file formats based on cost and performance, and work with databases like Redshift and DynamoDB, depending on your use case.

Beyond processing and storage, the certification also covers how to organize data so it’s easy to find and maintain across projects. On the security side, you’ll explore how to manage access using IAM, secure network connections, and protect your databases following AWS best practices.

This certification is ideal if you're looking to specialize in cloud-first data workflows and understand how to design reliable and secure systems on AWS.

AWS Certified Solutions Architect (Associate level)

The AWS Certified Solutions Architect certification focuses on designing cloud solutions, including data architectures. It’s a strong choice for data engineers who want to learn how to build secure, reliable, and scalable systems on AWS.

You’ll get hands-on experience with core services like EC2 for compute, S3 for storage, and AWS IAM for managing access and security. These are all key parts of setting up and running cloud-based data pipelines.

AWS Certified Developer (Associate level)

While the AWS Certified Developer certification is aimed at developers, it’s also helpful for data engineers who work on data pipelines that involve application integration.

You’ll learn how to:

• Build and deploy serverless apps using tools like AWS Lambda, Amazon SQS, and API Gateway
• Follow AWS security best practices with services like IAM (for access control), KMS (for key management), and MFA (for multi-factor authentication)

AWS Certified Machine Learning (Specialty level)

The AWS Certified Machine Learning certification is useful if you work with machine learning models or collaborate with data science teams. It focuses on the early stages of the machine learning pipeline — the parts where data engineers play a key role. In this, you’ll learn how to:

• Prepare data for ML, including cleaning and transforming datasets and creating features for training.
• Use core AWS tools like Amazon Kinesis to collect streaming data and Amazon SageMaker to train and deploy models.

Exam Format and Requirements

Once you’ve prepared, it’s time to take the exam. AWS exams validate how you apply your knowledge in real situations. They test your ability to make decisions and solve problems using AWS services.

Prerequisites and recommended experience

You don’t need official qualifications to take the exam — anyone can try it. But AWS does recommend a few things to help you feel more prepared:

• Hands-on experience: Ideally, you’ve spent at least a year working with AWS on real projects. Maybe you’ve helped build reliable, scalable systems or supported teams who did.
• Knowledge of core AWS Services: You should know the basics of services related to compute, storage, networking, and databases — enough to understand what each one does and when to use it.
• Basic architectural understanding: It helps if you can analyze a problem, understand the technical needs, and determine which AWS services are the right fit.

Exam details

Here’s what you can expect on the exam day for most certifications:

The total cost to appear in the AWS Associate-level exam is $150, and $300 for the Specialty ones (subject to change with time, so check exam pricing for more details). 

When you’re ready to book an exam, go to aws.training, login, and go to the Certification section. From there, you can schedule your exam through Pearson VUE or PSI. 

Key Topics to Study

Each AWS certification covers a broad range of concepts, but some topics come up more often and are worth spending extra time on. Let’s see what they are: 

AWS core services

These are some of the core services you’ll need to understand for most AWS certifications and for working with real systems in the cloud. They form the foundation for building and monitoring data infrastructure:

• Amazon S3 (Scalable object storage): It stores data like files, logs, or backups, and accesses them reliably at any scale. You’ll see it used in data lakes, pipelines, and as a landing zone for raw or processed data.
• Amazon EC2 (Virtual servers): They run computing workloads on-demand. You can adjust the instance size based on performance needs, which makes it ideal for scalable processing.
• AWS Lambda (Serverless compute): It runs code without managing servers. Lambda is event-driven, which means it’s perfect for lightweight automation and responding to triggers in a pipeline.
• Identity and Access Management: It defines who can access what in your AWS environment. This way, you can secure your infrastructure and ensure only the right people or services can interact with your resources.
• Amazon CloudWatch (Monitoring and observability): It tracks performance, sets alerts, and allows you to view logs from your AWS services. As a result, you can spot issues and keep systems running smoothly.

Data services

These are some of the key AWS services used in modern data pipelines. You’ll need to know what each one does and how they work together to collect and analyze data: 

• Amazon Redshift: It is a managed data warehouse that runs fast, complex queries across large datasets. 
• Kinesis: It collects and processes streaming data in real time which is ideal for use cases like live dashboards and fraud detection.
• AWS Glue: It transforms and moves data without managing infrastructure. This way, we can prepare data for analytics and machine learning pipelines.
• Amazon EMR: It runs big data workloads using tools like Apache Spark, Hive, or Hadoop. This makes it easier to scale and manage clusters.
• Athena: It runs SQL queries directly on data stored in S3. It’s serverless, so there’s no setup which is quite helpful for quick insights or ad-hoc analysis without complex ETL.

Data lake and ETL concepts

A data lake is a central place to store large amounts of structured and unstructured data. On AWS, services like S3, Lake Formation, and Glue make it easier to build and manage data lakes that support analytics and machine learning at scale.

We have to use ETL pipelines to bring data into a lake (short for Extract, Transform, and Load). These pipelines:

• Extract data from different sources
• Transform it into a usable format
• Load it into your data lake or data warehouse

AWS Glue makes it easier to build and manage these workflows. Since it’s serverless, we don’t have to handle infrastructure, and it supports orchestration, which automates and scales our data movement end-to-end.

Security and compliance

Security is a big part of working with AWS when we handle sensitive or large-scale data. Here are some key areas to focus on when studying or designing secure systems:

• Encryption with KMS: AWS Key Management Service (KMS) lets us create and manage encryption keys. It protects data from API keys to stored files, whether it’s at rest or in transit.
• Access control with IAM: IAM defines who can do what. Instead of long-term credentials, IAM roles grant temporary access, which is a safer approach. However, we must regularly review permissions to follow the principle of least privilege.
• Multi-factor authentication (MFA): Adding MFA is a simple way to protect high-risk accounts and stop unauthorized access.
• Monitoring and compliance: We use tools like CloudTrail and CloudWatch to track changes and spot unusual behavior. They help us comply with internal and industry standards.

Preparation Strategies

After reading this far, you may feel like there’s a lot to take in. But with the right approach, it’s completely manageable. So, I’ve divided the preparation process into three key areas so you can take a structured approach.

Study materials and courses

Start with AWS’s resources because they offer over 600 free digital courses and official exam guides for each certification. These are a perfect way to understand the exam topics and the key concepts you’ll be tested on.

In addition, you can also explore DataCamp, which offers beginner-friendly courses like AWS Concepts and AWS Cloud Technology and Services Concepts to help you build a strong foundation.

I suggest mixing official and third-party content to get a more rounded view, as this will help you find a better-aligned learning style.

Hands-on practice

Hands-on practice helps build confidence. AWS offers a Free Tier that gives you access to many services at no cost so that you can try things out in a real environment. You can use it to build small projects that put your learning into action. For example:

• Set up a static website using S3 and CloudFront
• Create a basic data pipeline using S3, Glue, and Redshift
• Trigger serverless functions with Lambda and CloudWatch

These practical exercises help reinforce what we’ve learned and prepare us for real-world scenarios that may appear in the exam and on the job.

Practice exams and mock tests

Once you’ve worked through the study material and gained hands-on experience, practice tests are the next step. They test your knowledge and make you comfortable with the exam format.

Here’s where to start:

• AWS official practice exams: These mirror the format and difficulty of the real test, so we know what to expect.
• Bonus prep: Practice answering 50 AWS interview questions to sharpen your thinking and get comfortable talking through your knowledge.

Career Impact and Next Steps

Once you’ve passed the exam, the real value starts to show in what you know and what it can unlock for your career. 

How a certification adds value

Employers often look for AWS certifications as proof that you understand cloud tools and can apply them in real-world scenarios. It can also open doors to better offers and more opportunities to grow.

Beyond job applications, it builds credibility. It shows you’re serious about your skills and helps you get noticed in the broader data engineering community.

If you want to go further, DataCamp’s Data Engineer career certification is another strong option. It’s designed to test real-world skills and give you a credential that hiring managers trust.

Continuing education and advanced roles

Once you've earned your AWS qualification, you can work towards other advanced roles, such as solutions architect, data scientist, or machine learning specialist.

If that’s your next step, DataCamp’s learning tracks can help you grow in the right direction. Paths like Data Scientist or Machine Learning Scientist will give you the skills and structure to take on more technical, high-impact roles.

And since AWS certifications need to be renewed every few years, it’s worth keeping your knowledge fresh. DataCamp also offers ongoing certification support to help you stay current as tools and best practices evolve.

Final Thoughts

AWS data engineer certification is a smart step toward skill progression and career growth. Start by understanding the certifications that match your experience, build a study plan, and get hands-on using AWS’s Free Tier.

Set a goal, stay consistent, and use what you build along the way — it all adds up to a certification that proves you can do the work.

And if you're keen to keep learning, here are some great resources from DataCamp to support your journey:

• AWS Cloud Technology and Services – a solid introduction to core AWS tools and concepts.
• AWS Cloud Practitioner (CLF-C02) – a foundational track to prepare you for the official AWS exam.
• Data Engineer in Python – a career track focused on building pipelines, working with big data, and using cloud tools.
• Associate Data Engineer in SQL – perfect to strengthen your SQL skills in real-world scenarios.
• Professional Data Engineer in Python – an advanced track to deepen your skills and take on more complex data challenges.