How to Become an AWS Data Engineer 2024 : Cloud Data Engineer

How to Become an AWS Data Engineer 2024 – A Complete, Step-by-Step Career Guide for 2023. Amazon Web Services (AWS) is like the boss of the cloud services game, with a huge 31% market share. It’s the go-to choice for big tech companies like Reddit, Twitch, Netflix, and Airbnb. AWS has a whopping 175+ services to offer, making it super user-friendly. These days, companies are scrambling to find AWS data engineers who can handle all the nitty-gritty of building and maintaining their AWS cloud infrastructure, so their apps can stay up and running smoothly. It’s a big deal!

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Imagine a world where data analysis is a breeze, and you have the key to unlock its full potential. With the combination of Pyspark, AWS EMR, and Athena, you can embark on an exhilarating journey of uncovering insights and making informed business decisions.

In this dynamic era of cloud computing, managing data has never been more exciting. Cloud platforms have revolutionized the way organizations handle their data, offering unparalleled convenience and agility. AWS, a trailblazer in the field, brings you a suite of cutting-edge solutions designed to simplify data management and unleash your true potential.

Explore the boundless possibilities of AWS Data Engineering, where you’ll effortlessly navigate through complex Data Pipelines, seamless Data Transfer, and reliable Data Storage. Gone are the days of grappling with the intricacies of data analysis. Let AWS empower you to focus on what truly matters – expanding your horizons and propelling your business forward.

Join the ranks of successful data professionals who have harnessed the power of AWS Data Engineering. Elevate your data analytics game and unlock a world of endless opportunities.

Are you ready to embark on this incredible adventure? The future of data analysis awaits!

Who is an AWS Data Engineer?

Data engineers generally need to have a working knowledge of software engineering and database management. Data engineers design, build, and maintain massive databases that support web applications or other digital services.

AWS data engineers perform the same duties as regular data engineers but exclusively on the Amazon Web Services cloud platform. In other words, an AWS engineer creates, maintains, and upgrades the AWS infrastructure to run applications. To succeed in this field, one should have a solid understanding of AWS and data engineering principles.

What Does an AWS Data Engineer Do?

AWS engineers are responsible for a wide range of tasks. An AWS data engineer, for example, is responsible for preserving data integrity and building data models to collect information from various sources. Furthermore, an AWS engineer looks for patterns in data that can be used to guide business decisions and design strategies. Finally, AWS developers continually look for new technologies and data sources to incorporate into existing or future projects. They regularly update outdated code and add new functionality.

AWS Data Engineer 2024 Job Description

An AWS engineer job description may vary from company to company. However, the following are some basics that fit any AWS data engineering job:

• Use one or more of AWS’s data and analytics tools in collaboration with third parties (e.g., Spark, AWS EMR, DynamoDB, AWS RedShift, AWS Kinesis, Lambda Functions, AWS Glue, AWS Athena, and Snowflake Data Warehouse) to design, develop, and operationalize large-scale enterprise data solutions and applications.
• Use AWS or third-party tools to analyze, re-architect, and re-platform on-premise data warehouses to data platforms in the AWS cloud.
• Using Java, Python, and Scala, design and construct production data pipelines from intake to consumption within a significant data architecture.
• Using AWS native or custom programming, design and implement data engineering, ingestion, and curation functions on the AWS cloud.
• Carry out thorough analyses of the data platforms in use today and design a suitable path for moving them to the AWS cloud.

AWS Data Engineer Roles and Responsibilities

AWS data engineers have a variety of responsibilities, some of which are:

• Constructing data models that can be used to gather information from numerous sources and store it in a helpful fashion.
• Maintaining data integrity through the development of backup and recovery mechanisms.
• Finding ways to boost performance by enhancing database design.
• Researching new technologies and data sources that can be used in projects currently being worked on.
• Finding patterns or insights in data that can be used to inform business choices or build strategies.
• Creating new applications that employ existing datasets to provide new goods or improve current services.
• Upgrading old code or adding new features to existing apps to keep them up to date with changing needs.
• Designing and implementing security measures to protect data from misuse or unauthorized access.
• Making improvements to the infrastructure to increase storage capacity or performance.
• Building data pipelines and managing large datasets.
• Using AWS tools to integrate data.
• Using Amazon Simple Storage Service to retrieve any or all of the data.
• Implementing firewall security on AWS using AWS security groups.

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Why Should You Consider a Career as an AWS Data Engineer? 

There are several reasons to consider a career in AWS, whether you are new to cloud computing (or tech, for that matter) or an experienced tech professional looking to steer your career in a new direction.

Reason # 1: AWS is Rapidly Growing

AWS had a seven-year advantage over its rivals because it was the first public cloud service to launch in 2006. AWS capitalized on this advantage to the fullest and has been the public cloud provider with the most significant growth rate ever since. For example, it has continued to expand dramatically, with third-quarter growth of 39% in 2021.

Reason #2: Growing Importance of Cloud Computing

It’s expected that more people will continue to use machine learning (ML) and artificial intelligence (AI) in the cloud well beyond 2022. Additionally, about 50% of tech professionals think that AI and machine learning will significantly impact the adoption of cloud computing. Because it is the most commonly utilized public cloud computing service, employed by both large and small businesses, learning AWS is now crucial for tech professionals who want to secure their future job.

Reason #3: Job Opportunities for Everyone

One major advantage of X is the wide range of job opportunities it provides. Whether you are just starting out in your career or looking for a change, X offers a diverse and dynamic job market. With a multitude of industries and sectors, there are endless options for individuals with different skills and interests. From technology and finance to healthcare and creative arts, X offers opportunities for people from all walks of life. So, whatever your passion may be, you are likely to find a fulfilling and rewarding job in the world of X.

‘AWS engineer’ is a broad term with various sub-titles. AWS engineers can be data scientists, back-end or front-end developers, full-stack developers, devops engineers, and more! Moreover the demand for AWS Data engineers outstrips the available skilled AWS professionals making it a top career choice in 2023 and beyond.

Guide To Becoming a Certified AWS Data Engineer

Welcome to our comprehensive guide on how to become a certified AWS Data Engineer! In this guide, we will walk you through the necessary steps and resources to help you pave your path towards achieving this certification.

Why Become an AWS Data Engineer?

Before we begin, let’s explore why becoming an AWS Data Engineer is a great career move. With the ever-increasing demand for businesses to effectively manage and analyze data, the need for skilled data engineers has skyrocketed. AWS, being one of the leading cloud service providers, offers a robust suite of tools and services for handling big data and analytics, making their certification highly sought after in the industry.

Prerequisites and Knowledge Requirements

To embark on this journey, it is essential to have a solid foundation in computer science and a basic understanding of AWS and its services. Prior experience with databases, data processing frameworks, and programming languages such as Python or Java will be advantageous. Additionally, familiarity with concepts like data warehousing, data modeling, and ETL (Extract, Transform, Load) processes will prove beneficial.

Certification Path

To become a certified AWS Data Engineer 2024 , you should follow these steps:

1. Foundational Certification: Start by obtaining the AWS Certified Cloud Practitioner certification. While not mandatory, it provides a solid understanding of AWS fundamentals and sets the stage for further specialization.
2. Associate Level Certification: The next step is to pursue the AWS Certified Data Analytics – Specialty exam. This certification validates your expertise in designing, building, and maintaining big data solutions using various AWS services.
3. Professional Level Certification: After successfully completing the associate level, you can aim for the AWS Certified Big Data – Specialty certification. This advanced-level certification focuses on developing and deploying scalable, secure, and fault-tolerant data solutions using AWS big data services.
4. Experience and Continuous Learning: Alongside certifications, gaining hands-on experience with real-world projects is crucial for honing your skills as a data engineer. Stay updated with the latest advancements in AWS and the broader data engineering field through continuous learning, attending webinars, and participating in relevant communities.
5. Specialization and Advanced Training: As you progress in your career, consider exploring specialized certifications like AWS Certified Machine Learning – Specialty or AWS Certified Database – Specialty to further enhance your credibility in specific domains.

Recommended Resources and Study Materials

Preparing for AWS certifications requires a combination of theoretical knowledge and hands-on practice. Here are some valuable resources to help you along the way:

• AWS Documentation: Official AWS documentation is an excellent starting point to understand the services, architectures, and best practices related to data engineering on AWS.
• AWS Training and Certification: AWS offers comprehensive training courses, practice exams, and sample questions specifically designed to prepare you for their certifications. Take advantage of these resources to deepen your understanding of AWS data engineering services.
• Online Learning Platforms: Platforms like A Cloud Guru, Coursera, and Udemy offer a wide range of courses and tutorials tailored to AWS certifications. Look for courses that specifically cover data engineering and analytics topics.
• Hands-on Projects: Set up your own AWS account and practice implementing data engineering solutions using services like Amazon S3, Redshift, Glue, and Athena. Building real-life applications will greatly reinforce your skills and confidence.

Remember that certification exams may include scenario-based questions, so it is essential to have practical experience in addition to theoretical knowledge.

1) Learn About AWS Products and Services

If you are interested in working as an AWS cloud engineer, you ought to know of the products and services AWS provides.

AWS solutions can be categorized into 3 categories: Deployment & Management, Application Services, and Foundation Services.

Must Know Tools for AWS Data Engineers

There are many AWS tools dedicated for data engineering, some of the popular ones being AWS Glue, AWS Redshift, AWS Kinesis, AWS Athena and AWS IAM.

AWS Glue

AWS Glue is a serverless data integration solution that facilitates the discovery, preparation, transportation, and integration of data from various sources for data analytics, machine learning, and application development. Amazon utilizes AWS Glue to consolidate data warehouse and business intelligence technology.

Here’s a Guide on ” How to Become a GCP Data Engineer“

AWS Athena

AWS Athena is a query service that allows you to quickly examine data in Amazon S3 using conventional SQL. Because Athena is serverless, there is no infrastructure to manage, and you pay for the queries you execute.

Athena is simple to use. Point to your Amazon S3 data, configure the schema and begin querying using regular SQL queries. The majority of results are supplied in seconds. There is no need for sophisticated ETL procedures to prepare your data for analysis with Athena. This enables anyone with SQL knowledge to swiftly evaluate large-scale datasets.

AWS Redshift

Amazon Redshift uses SQL queries to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes using AWS-designed hardware and machine learning to deliver cost-effective performance at any scale. AWS Redshift also automatically recognizes Star Schema data structures and includes built-in optimizations to search this data effectively.

AWS Kinesis

Amazon Kinesis enhances the collection, processing, and analysis of real-time streaming data to provide users with timely insights and quick access to new information. It allows the ingestion of real-time data for ML and data analysis. Additionally, Kinesis has a sub-tool called Amazon Kinesis Data Firehose used for data ingestion.

AWS Identity and Access Management (IAM)

AWS Identity and Access Management (IAM) allows you to specify who or what can access AWS services and resources, manage fine-grained permissions centrally, and analyze access to adjust permissions across AWS.

For more tools designed for data engineering, check our article ‘10 Popular AWS Services for Data Engineering‘.

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2) Practice AWS Projects

‘Practice makes perfect’ always applies when it comes to improving your technical skills, and AWS Data Engineer 2024 is no different. To have a solid skill-set in AWS, you must have hands-on experience implementing different data engineering applications. As such, here is a list of of some beginner level AWS projects you can work on.

Level 1: Serverless Web Application (Beginner)

The purpose of this AWS project is to use AWS to create and deploy a safe and valuable serverless web application (using serverless functions in Amplify, AWS Lambda, etc.). To complete the project, you can use a variety of tools such as AWS Amplify -for front-end and hosting), AWS Cognito -for backend authentication and administration, DynamoDB – for data integration in a persistence layer for storage, and AWS Lambda functions and API Gateway -for backend API.

To be able to successfully create this project, you should have hands-on experience in HTML, JavaScript, CSS, and RESTful API implementation. 

Level 2: Real-Time Data Processing Application (Intermediate)

The goal is to handle large amounts of data in real-time while maintaining the accuracy of the results. Bustle is a real-world example that uses Amazon’s services to process enormous numbers of site-statistic data in real time. You can work on this project with Amazon Kinesis Stream and AWS Lambda. You will be required to construct a Kinesis Stream in the first stage and configure it to capture data from a web source.

Level 3: Custom-Made Alexa (Advanced)

The purpose of this project is to develop a virtual assistant similar to Amazon Alexa. It should replicate the skills and function of Alexa and you can add a few ones of your own. Using AWS Lambda can facilitate the development of this project.

3) Earn an AWS Certification

Amazon offers multiple certifications for those interested in an AWS big data engineer career. There are four types of AWS certifications; Foundational, Associate, Professional, and Specialty, each targeting a specific skill-level.

• Foundational: 6 months of experience in fundamentals of AWS Cloud & industry knowledge
• Associate: One year of experience in solving problems & implementing solutions using AWS Cloud
• Professional: Two years of experience designing, operating, and troubleshooting solutions using AWS Cloud.
• Specialty: Technical AWS Cloud experience in the Specialty domain.

For more information about the AWS certifications, check out the AWS website.

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4) Prepare for the AWS Data Engineer 2024 Interview

There are many things to consider when preparing for the AWS Engineer interview. The interview is a chance to learn more about AWS and the company culture. The interview is also used to assess your ability to work in virtual teams, describe coding patterns and applications you have developed, and your technical knowledge of the technologies being used.

If you want to land an AWS Data Engineer 2024 job and need to know how to prepare for an AWS engineer interview, these tips will help you.

Tip #1: Do Your Research

Just like for any interview, it is important to properly search the company you are applying for, AWS included. While it is not the most important aspect of the interview process, having solid information about the company will make a good first impression that you have done your research.

Questions about the company may include:

• What do you know about AWS?
• Why did you choose to apply for this job in AWS?

Tip #2: Talk About Your Accomplishments

Having an AWS certification is not enough to impress AWS recruiters. In fact, many candidates don’t pass the interview because they were not specific on what they have accomplished, aside from getting the certificate. You should focus more on talking about how you reached your achievements rather than describing what they are.

Tip #3: Know The Commonly Asked Questions

There are multiple sources online that discuss the most commonly asked questions in AWS interviews. Some of them being:

1. What are the AWS components?
2. What types of instances are you familiar with?
3. What are the restrictions on AWS Lambda function code?

These are just a few of the many questions you could be asked in an AWS interview. It is worth noting that the interview is slightly difficult. However, do not let this intimidate you, don’t be afraid of informing the interviewer if you don’t know an answer, and try to seem as confident as possible.

Must-Haves on an AWS Data Engineer 2024 Resume

According to ZipRecruiter, a specific set of keywords is used in most AWS engineers’ resumes, making them a must-have. For example, Amazon Web Services is the most commonly found skill on resumes, followed by Database, AWS Redshift, Data Warehousing, and Data Modeling.

An AWS engineer resume should also include skills like writing data using AWS EMR, understanding different database schemas -such as the Snowflake schema, sound knowledge of distributed systems, etc. Additionally, the resume must also include at least three relevant projects.

An example of these AWS projects could be:

1. Developing a virtual assistant with similar skills and functions to Amazon’s Alexa
2. Creating a real-time end-to-end AWS log analytics solution to collect, ingest, and analyze data. 
3. Building an E-commerce analytical platform using AWS services like S3, Amazon Glue, and Amazon Kinesis to gather all information that impacts the retail outlet. 

That said, recruiters are looking for more than these keywords and projects in a AWS Data Engineer 2024 resume. An AWS engineer is expected to have skills like:

• Communication: to explain complicated technical terms and processes to non-technical professionals in a company, such as managers or executives.
• Critical thinking: to help in researching, planning and developing software and cloud applications to meet the requirements.
• Organization: to prioritize tasks and handle workloads to meet deadlines.
• Problem-solving: to troubleshoot technical issues and quickly come up with solutions to protect important data.
• Teamwork: to collaborate effectively with other teams, such as developers, systems administrators or IT managers.

Now is the Best Time to Become an AWS Data Engineer

Becoming an AWS data engineer involves making important choices about which tools to use. It can be overwhelming with so many options available. By being well-prepared and knowledgeable, you can confidently navigate this decision-making process. Writing code is a valuable skill for this role, allowing you to solve complex data engineering problems. Stay curious and continuously seek new challenges to fuel your creativity.