Are you dreaming of a high-paying tech career but don’t know where to start? Data Engineering on AWS is one of the fastest-growing fields, offering endless opportunities. Companies need experts to build, manage, and optimize data pipelines — and that could be you! This guide will take you from zero to hero, helping you gain the skills to land your first Data Engineering job.

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Why AWS for Data Engineering? Career Growth & Technical Advantages

The need for skilled Data Engineers has never been greater. As companies generate more data and shift to cloud solutions, they require experts to build and maintain data pipelines in these new environments. In fact, 94% of companies worldwide now use cloud computing in their operations​, and many are migrating their data infrastructure to the cloud. The largest cloud provider AWS is at the forefront of this shift – it dominates the global cloud market with about a 32% share​. This widespread adoption of AWS for data-driven decision-making is fueling a surge in demand for professionals who can leverage its services. It’s no surprise that “Data Engineer” was cited as the fastest-growing tech job​. Companies across the globe are actively seeking AWS-savvy data engineers to help them harness data for insights, making this one of the hottest careers in tech today.

When it comes to data storage, ETL, and analytics, AWS offers technical advantages that make these tasks highly scalable and cost-effective. The AWS ecosystem provides an extensive array of managed services that simplify each step of the data engineering process. Here are some key AWS services that give data engineers a superior toolkit:

Together, these services illustrate AWS’s technical superiority: you get an integrated environment where storage, processing, and analytics services work together, scale on demand, and minimize management overhead. It’s this robust toolkit and flexibility that have made AWS a favorite platform for data engineering teams worldwide

Building expertise in AWS data engineering can fast-track your career and earning potential. Employers are willing to pay top dollar for cloud data skills – AWS Data Engineers are in high demand across startups and enterprises alike. Many of these roles come with competitive, often six-figure salaries. For example, in the United States, the average AWS Data Engineer earns around $130,000 per year​, with experienced engineers earning even more.

Every big journey starts with a single step. Now is the perfect time to start your AWS data engineering journey. Whether you aim to land a high-paying job or to advance in your current role, acquiring AWS data skills will open doors. Don’t just read about the cloud revolution – become a part of it. Get hands-on, get certified, and transform your career trajectory.

Skills You Need to Become a Data Engineer on AWS

As a beginner aiming to go from zero to hero in AWS data engineering, you’ll need to build a mix of technical expertise and soft skills. In this section, we’ll break down the essential AWS Data Engineer skills — from mastering AWS data pipelines and big data frameworks to sharpening your communication and problem-solving abilities — and highlight a few AWS certifications that can give your career a boost.

Technical Skills You Need to Master

You’ll be working with large datasets and cloud tools, so start by building a strong foundation in these technical areas:

Soft Skills That Make a Difference

A Data Engineer’s job isn’t just about writing code and managing cloud resources. It’s about understanding how data flows, troubleshooting complex issues, and ensuring that business teams can actually use the data they need. You might build the most efficient pipeline in the world, but if it fails unexpectedly, costs too much, or delivers data that analysts can’t interpret, your work isn’t done.

Take a real-world scenario: A company’s sales dashboard suddenly stops updating. The data pipeline feeding it was working fine yesterday, but today, the numbers are frozen. A strong Data Engineer doesn’t just restart the process and hope for the best. Instead, they trace the issue step by step—checking data ingestion logs, verifying AWS Glue jobs, optimizing Redshift queries, and ensuring that IAM permissions haven’t changed.

This is where problem-solving and debugging become invaluable. Pipelines break, queries slow down, and storage costs skyrocket unexpectedly. Being able to diagnose inefficiencies, fix failures, and design systems that are resilient makes all the difference. Experienced Data Engineers don’t just react to problems—they anticipate them and build safeguards to prevent them from happening in the first place.

But problem-solving alone isn’t enough. A Data Engineer needs to bridge the gap between raw data and business insights, which means working closely with data scientists, analysts, and decision-makers. Communication is often the missing link. Can you explain to a business executive why a query takes minutes instead of seconds? Can you help a data analyst understand how to structure their reports for faster processing? The ability to translate technical complexities into simple explanations is just as valuable as your coding skills.

Beyond communication, project management and prioritization play a crucial role in data engineering success. It’s easy to get caught up in writing scripts and optimizing queries, but what happens when multiple teams rely on your pipelines, and deadlines overlap? A skilled Data Engineer knows how to balance urgent fixes with long-term improvements, ensuring data infrastructure stays scalable, cost-efficient, and reliable.

AWS Certifications to Boost Your Career

While hands-on experience is the most valuable asset in data engineering, AWS certifications provide an edge, proving to employers that you understand AWS services deeply and can design scalable solutions. If you’re looking to validate your expertise, these three certifications are particularly useful:

📌 AWS Certified Data Analytics – Specialty
This certification demonstrates expertise in AWS data services like Glue, Redshift, Kinesis, and QuickSight, proving that you can design and manage complex analytics workflows.

📌 AWS Certified Solutions Architect – Associate
Even though it’s not data-specific, this certification teaches core cloud architecture principles, including scalability, security, and cost optimization — all of which are crucial for a Data Engineer working in AWS.

📌 AWS Certified Big Data – Specialty (Retired but still relevant)
Although no longer offered, this certification covered Spark on EMR, data lakes, security best practices, and real-world big data scenarios. If you can find study materials for it, the knowledge is still highly relevant for modern AWS data engineering workflows.

AWS certifications won’t make you a Data Engineer overnight, but they can accelerate your learning, boost your credibility, and open doors to new job opportunities.

Step-by-Step Roadmap: From Zero to Hero in Data Engineering

Step 1: Build a Strong Data Foundation (1-2 months)

Start by understanding how data is stored, managed, and processed. Learn the basics of databases and practice SQL to retrieve, manipulate, and optimize data efficiently. At the same time, refresh your knowledge of data structures such as arrays, lists, and hash tables, which influence data processing performance.

AWS is the industry leader in cloud computing, so take the time to understand why companies are migrating to the cloud and the key benefits of AWS. Get familiar with basic cloud concepts like scalability, security, and serverless computing to understand how cloud platforms handle big data workloads.

Focus areas:

✅ By the end of this step, you should understand fundamental data concepts and have a high-level understanding of AWS services.

Step 2: Master Python & Programming for Data Engineering (2-3 months)

With the fundamentals in place, focus on programming — Python is the industry standard for data engineering due to its powerful libraries for data processing and automation.

Start by writing basic Python scripts that parse data files, call APIs, and manipulate structured datasets. Use libraries like pandas for data analysis and boto3 to interact with AWS services. Alongside Python, keep refining your SQL skills—download real-world datasets and practice writing complex queries.

Version control and automation are also essential. Learn Git for tracking code changes and basic shell scripting to automate data workflows.

Focus areas:

✅ By the end of this step, you should be comfortable writing Python and SQL scripts for data processing and automation.

Step 3: Learn ETL and Data Pipeline Design (2-3 months)

Data Engineers move and transform data efficiently. This step focuses on ETL (Extract, Transform, Load) pipelines — which are at the core of data engineering.

Start by designing a simple ETL workflow — pulling data from a source, processing it, and loading it into a database. Explore batch vs. real-time data processing and when to use each.

Another key skill is data modeling — learn how to design efficient schemas for relational databases and data warehouses. Study concepts like normalization, indexing, partitioning, and denormalization to ensure fast queries and optimal storage.

Key Focus Areas:

✅ By the end of this step, you should be able to design and implement basic data pipelines and optimize data storage for performance.

Step 4: Master AWS Data Engineering Tools (3-4 months)

Now it’s time to dive deep into AWS-specific tools. Start with data storage services, as they form the backbone of AWS data engineering.

Next, explore AWS data processing services:

Hands-on practice:

✅ By the end of this step, you should be able to store, process, and analyze data efficiently using AWS services.

Step 5: Build Real-World Projects (2-3 months)

Nothing solidifies learning like hands-on experience. Now, build an end-to-end data pipeline to showcase your skills.

Project idea 1 – batch data pipeline:

Project idea 2 – real-time data pipeline:

Best practices:

✅ By the end of this step, you should have a portfolio-ready project to showcase in job applications.

Step 6: Learn DevOps & Automation (1-2 months)

Deploying scalable data pipelines requires automation and infrastructure management. Learn Infrastructure as Code (IaC) to define AWS resources programmatically.

Hands-on practice:

✅ By the end of this step, you’ll be able to deploy and maintain data pipelines efficiently.

Step 7: Prepare for AWS Certifications & Job Applications (1-2 months)

Certifications help validate your expertise and increase your credibility in the job market.

Job application readiness:

✅ By the end of this step, you’ll be job-ready with a strong portfolio and AWS certification (optional but valuable).

Career Transition Tips: How to Land Your First Data Job

Breaking into your first data role can feel challenging, but with the right approach, you can go from beginner to professional. Whether you’re aiming for an AWS Data Engineering position or another data role (like analyst or scientist), the following strategies will help you showcase your skills and land that first job. These tips are actionable, motivating, and easy to follow – so let’s dive in!

Build a Strong Resume

Your resume is often the first impression, so make it count. Highlight the skills and experiences that prove you’re ready for a data role, especially any hands-on work with AWS:

(Tip: If you lack formal work experience, feature academic projects or personal projects in your experience section. A capstone project where you built a small data warehouse on AWS or analyzed a dataset can demonstrate the same skills a job would – just present it like you would a job entry.)

Network Strategically

Landing a job in data often comes down to who you know as much as what you know. Networking can uncover hidden opportunities and get your resume in front of the right people:

(Remember: Many data job openings aren’t publicly advertised – they’re filled via referrals or internal networks. Every new person you connect with is a potential link to an opportunity. So, cast your net wide and nurture those professional relationships.)

Ace the Interview Process

Preparing for interviews is crucial, both the technical grilling and the behavioral questions. Here’s how to be ready for anything a data interview panel might throw at you:

(Bonus tip: Treat interviews as learning experiences. After each interview, jot down the questions you were asked and reflect on how you answered. If you stumble on a question about, say, a specific AWS service or a statistic concept, use it as motivation to fill that gap in your knowledge before the next interview. Continuous improvement will eventually land you the offer.)

Develop a Portfolio of Projects

Having a portfolio is powerful for newcomers—it gives recruiters proof of your skills beyond the resume. A strong portfolio showcasing real or realistic projects can set you apart from other entry-level candidates:

(Pro tip: Put a link to your portfolio or GitHub in your resume and LinkedIn profile. Recruiters do click these links. Seeing a clean code repository or a live demo of your project can sometimes impress them even more than your resume bullets do.)

Take Action Today!

Now is the time to take action. Set learning goals, apply your skills in real-world projects, and showcase your work. Whether you’re just starting or refining your expertise, every step you take brings you closer to your goal. Stay consistent, keep learning, and engage with the data community — opportunities will follow. Your future as a Data Engineer starts today. Take that first step, build something great, and land the career you’ve been working toward!

Chris Garzon

Christopher Garzon has worked as a data engineer for Amazon, Lyft, and an asset management start up where he was responsible for building the entire Data Infrastructure from scratch. He is the author “Ace the Data Engineer Interview” and has helped 100’s of students break into the data engineer industry. He is also an angel investor, an advisor to multiple to multiple start ups, and the founder and CEO of Data Engineer Academy.