In the cloud services and data engineering space, Amazon Web Services (AWS) is the leader, with a market share of 32%. With a collection of robust tools and services that help businesses handle data at scale, AWS has become the preferred service provider for some leading internet businesses, like Facebook, Netflix, LinkedIn, Twitch, etc. These companies are constantly looking out for professionals who are familiar with and can develop newer technologies and systems for larger volumes of data.
If you want to build a career in data technologies, then the AWS platform is right for you. However, this field is growing swiftly, and you need to stand out from the crowd. One of the best ways to do so is with certifications. Employers always prefer those on the Amazon AWS learning path.
So, if the question in your mind is how to become AWS data engineer, you’re in the right place. Here, we’ll answer that and many other AWS data engineer roadmap questions.
Who is an AWS Data Engineer and What Do They Do?
An AWS data engineer is typically an Information Technology (IT) professional. They obtain an AWS certificate to show their expertise and skills to work in an AWS-related position.
An enterprise stores data in a variety of formats, files, texts, databases, etc. A data engineer builds pipelines to help convert this data into readable and usable formats. It’s done in a manner that makes it easy to analyze. To carry out such duties, it’s imperative to understand database management and software engineering.
AWS data engineers also need to have a sound understanding of AWS. This comes through the AWS data engineer certification path.
How to Become an AWS Data Engineer? [In 5 Simple Steps]
Over the past few years, most companies have adopted digital transformation. As a result, they accumulate massive volumes of data that needs to be safely stored and analyzed. This has caused an increase in the demand for data engineer jobs. So, if this field interests you, let’s look at the AWS certification path for data engineers.
Step 1: Master Skills
Data engineers must know AWS to execute their tasks. But that’s just one aspect of it. There are numerous skills involved that help in the day-to-day operations of this role.
Top Skills Needed to Become an AWS Data Engineer
The top skills can be broadly divided into soft skills and hard skills. They’re as follows:
1. Soft Skills
Soft skills are not related to any specific job but help you excel in any workplace.
- Analytical thinking: When a problem arises, you use your analytical skills to collect relevant information, analyze the situation, and decide and solve issues to overcome the obstacles. In the AWS data engineer role, you work with large amounts of data, so having this skill is important to make data-driven decisions.
- Problem-solving: While problem-solving encompasses analytical thinking, it’s slightly broader. You must use your adaptability, resourcefulness, and creativity to develop innovative solutions. It’s very useful in your career as it helps to optimize processes quickly.
- Communication: Communication is when you convey or share ideas and feelings effectively. It’s extremely important because data engineers work with different teams. It’s important to be able to translate technical information into understandable language.
- Teamwork: As mentioned in communication, you work with cross-functional teams as data engineers. Being a team player is essential so you can collectively work towards the goal and collaborate without any hiccups.
- Attention to detail: When you pay attention to detail, you can carry out tasks accurately. This way, you avoid sloppy mistakes and deliver high-quality work. When you work with data, accuracy and precision are key.
2. Hard Skills
We’re categorizing hard skills into a few buckets. You need a majority of these during your career.
A. Data Engineering Skills
- Data modeling - It’s the process of creating a visual representation of data objects and their connections between data points and structures. You need this to help structure, organize, store, and retrieve data.
- Data warehousing - This is a central repository of information you use to analyze data and make decisions. You need to know the data warehousing concepts to make your job easy.
- Database management - A database is a tool to create and manage databases. You must be proficient in NoSQL and SQL for data engineers to help with database management.
- Data pipeline design - It's where you extract raw data from different data sources and export it for analysis. Data engineers must design efficient pipelines for easy transfer of data.
- Data integration - Data integration is when you combine data from multiple sources into a unified set. This is important as you will integrate data from external databases, APIs, and third parties.
B. Programming/Scripting Languages
- SQL: A programming language for storing and processing information. If you’re working with databases, you need a good grasp of SQL.
- Python: Another high-level programming language. Data engineers use this for tasks like automation, data manipulation, and scripting.
- Java (optional): A programming language typically used for coding web applications. Some organizations may ask you to work with Java.
C. Data Visualization and Analysis
- Power BI - A tool for analyzing and visualizing raw data to present actionable information. Knowing this helps you create data dashboards.
- Tableau - A platform that helps visualize and analyze data. You need this for reporting and data visualization.
- Amazon QuickSight - Very similar to the above two, and it’s needed for data visualization and creating dashboards.
D. Other Competencies
- Data governance - You must understand rules and regulations of how data must be gathered, stored, processed, and disposed of to ensure accuracy, security, and compliance.
- Data compliance and privacy - Ensuring data is confidential and compliant with the law. So, you must be up to date on data privacy regulations.
Top AWS Data Engineer Certifications/Degree
MediumAWS offers a range of relevant certificates based on your individual needs. There are a few that have gained popularity. Here are some of them:
- AWS Certified Data Analytics (Specialty): This certification shows your ability to use AWS data analytics services and data lakes to gain insights from data. It’s best suited for those who know what AWS data analytics services are and how they work together. It’s also good to know how AWS data analytics services work at each stage of the data process: collecting, storing, processing, and displaying data.
- AWS Certified Database (Specialty): AWS Database certificate shows expertise in designing, maintaining, and recommending optimal AWS database solutions. It’s meant for those who can understand and differentiate the features of AWS database services. Besides this, design appropriate database solutions using AWS services.
- AWS Certified Solutions Architect (Associate): The AWS Solution Architect certificate focuses on the design of cost and performance-optimized solutions. It’s meant for those with experience working in other cloud services, experience and understanding of mapping on-premises to the cloud, and experience in AWS technology.
- AWS Certified Developer (Associate): This certificate showcases expertise in core AWS services and best practices and the ability to build, deploy, and troubleshoot cloud-based applications on AWS. It’s best suited for those with experience in AWS technology, in-depth knowledge of one programming language, experience working in other cloud services, and experience and understanding of mapping on-premises to the cloud.
- AWS Certified Cloud Practitioner: AWS Cloud Practitioner is the first step in AWS certifications. It shows you have a basic understanding of AWS Cloud, its services, and terms. It's great for beginners new to IT or the cloud and for people in non-technical roles who want to learn the basics of the cloud.
Step 2: Determine your Ideal Timeline
There is no specific timeline. The learning curve depends on your current knowledge, how much time you have to study, and how much expertise you want to gain. Here’s an overview of a standard timeline:
- Assess your starting point (1 to 2 months): See where you are and how much you know. Find out the gaps you need to fill.
- Set learning goals (1 month): You know what gaps you need to fill, so break it down into goals and certifications you want to achieve.
- Choose a learning path: Pick what works for you, whether it's online classes, self-study, or video tutorials. The time frame varies based on what form of learning you choose.
- Gain hands-on experience (3 to 6 months): Work on some projects and real-life tasks to help put theory into practice.
- Certification (Varies): Take the certification when you feel fully prepared. This can vary from individual to individual.
1. Start Learning in a Format That Suits You
Learning doesn’t always have to be in a classroom. You can choose any learning method if you put in the time and effort.
- Online tutorials and courses: You can gain your certification for AWS via courses. Further, many platforms offer useful tutorials online.
- Self-paced learning: This means you use online resources like ebooks/ blogs or physical books to learn at your own pace.
- Bootcamps or workshops: These forms of learning help with intensive and hands-on training.
- Local or virtual communities: Engage with like-minded people so you can talk about, discuss, and learn more about the topic.
2. How Long Does it Take to Become an AWS Data Engineer?
Again, the AWS data engineer learning path varies. It’s reliant on multiple factors like:
- Prior experience: Whether you already have experience in the field or not. And what level of expertise.
- Education: Do you already have formal education in the field? If yes, it will be beneficial.
- Learning pace: How quickly do you want to learn? Do you have time to spend hours studying, or will you juggle work and studies?
- Certification goals: The time level will vary depending on your chosen certification.
- Study commitment: Your commitment to studying regularly is important. Consistent learning will speed up the process.
- Preparation for the certificate: Depending on your knowledge, how much you study, etc., the time to prepare for a certificate can be several weeks to a few months.
From this, you can conclude that starting in the field of data engineering with minimal experience can take several months of focused learning and practice. The process may be faster if you’re somewhere in the middle of your career and has sufficient knowledge. And for experts, upgrading your knowledge is perhaps just a matter of weeks.
Step 3: Begin Building your Portfolio/Resume
In the AWS data engineer certification path, studying is one aspect; building a strong resume and portfolio is another to be successful. It helps you showcase your experience and skills to potential employers. Here’s a quick overview of building a portfolio/resume:
- Create a professional resume: Start by picking a professional-looking and well-organized resume format. Don’t forget to include your name and contact information here.
- Compelling career summary: Include a brief, concise summary at the beginning of your resume, highlighting your overall experience.
- List your AWS certifications: Clearly list all your certifications for Cloud Engineer, including the name and date. You can also add any other related course you’ve done.
- Showcase your skills: Write about the soft and technical skills you have to get the job done.
- Detail your work experience: Describe your previous work experience and roles related to AWS or data engineering. It’s good to use examples and include any contributions or achievements.
- Add your education: List your educational qualifications, especially if relevant to your job.
- Additional information: You can also include information like any workshops, bootcamps, mentor sessions, etc., that you’ve engaged in.
- Create an online portfolio: You can use your personal website or portfolio-building websites to showcase projects you’ve dealt with related to AWS. Insert this link in your resume.
Pro tip: Three important things you shouldn’t forget - proof your resume to avoid silly spelling mistakes, format it so it looks clear and readable, and seek feedback for finalizing your resume.
Step 4: Where and How to Find a Job?
During and after your AWS certification path for data engineering, you must employ a few strategic steps to find and land a job. Here’s some help:
- Company websites: Choose a company you’d like to work for and visit their careers page. Often, these companies have open roles you can apply for.
- Online job portals: Popular job search portals like Glassdoor, Indeed, or LinkedIn list our current open positions. You can narrow your search with relevant keywords like ‘AWS data engineer.”
- Networking: Connect with professionals in the field through forums, events, or social media. This can lead to job recommendations or referrals.
- Job fairs: Attending job fairs not only helps with recruitment but also with securing a job.
- Recruitment agencies: Some agencies specialize in recruiting for the IT field. They will help match your skills with a role.
- Company referrals: Ask those you know in the industry for any referrals. Referrals often get noticed by hiring managers.
Top Companies Hiring AWS Data Engineer
Here are the names of some of the top companies that hire AWS data engineers:
- Amazon Web Services (AWS): Amazon's cloud computing segment often requires AWS professionals.
- IBM: IMB offers cloud solutions and services and frequently hires data engineers with cloud experience.
- Accenture: This global consulting and technology firm recurrently hires those with cloud and data engineering skills.
- Deloitte: The consulting and technology division of the company has open cloud and data engineering roles.
- Tata Consultancy Services (TCS): A leading IT services and consulting company, TCS often has AWS Data Engineering roles.
- Infosys: Infosys has various cloud-related projects that require AWS expertise, for which they hire professionals.
- Startups: Several startups are entering into the cloud space and adopting AWS. They may have some exciting data engineering roles you can explore.
Step 5: Ace the Interview
To ace your interview, it’s best you know how the process works and keep some tips we have in mind. You’ll learn more as you read along.
1. Brief Overview of the Interview Process
If you want to stand out, staying well-prepared for the interview is important. Here’s what the interview process looks like:
- Screening: After your application, there will be an initial phone call or online meeting to assess your qualifications and interest in the role.
- Technical assessment: Depending on the company, you may need to complete a technical test related to data engineering or AWS.
- Technical interview: Upon passing the technical assessment, there’s a deeper technical interview where you may be asked to answer AWS-related questions, discuss your past projects, or solve problems.
- Behavioral interview: Here they will assess your soft skills and see if you're a good cultural fit for the organization.
- Final interview: In the final interview, you meet with senior members who will do a final evaluation.
Again, this is to give you an idea. The actual steps vary from company to company.
2. Tips for AWS Data Engineer Interview
Being nervous is not uncommon. However, with a few tips, you can remain calm and ensure a smooth interview process.
- Review all the AWS concepts thoroughly.
- Practice SQL, Python, and other languages.
- Know what you’ll talk about regarding your past projects and jobs.
- Ensure you highlight the key aspects of your role or certificates.
- Read mock interview questions to help you prepare.
- Be confident when you showcase your hard and soft skills.
- Ask questions about the company to show your interest.
Who Can Become an AWS Data Engineer?
This is for you if you are interested in the AWS data engineer learning path. You need to have a passion for data and an eagerness to learn. If you do, it’s a rewarding career path.
AWS Data Engineer Requirements
While it can vary from employer to employer, here are the typical requirements.
- A bachelor’s degree in a relevant field or equivalent practical experience
- Proficiency in AWS-related services like data engineering, Lambda, etc.
- Strong programming skills, especially in languages like SQL and Python
- Knowledge of the processes that AWS entails
- Certificates that validate your expertise
- Excellent hard skills like technical capabilities and soft skills like communication and team player.
Why Should You Choose the AWS Data Engineer Role?
Besides the field growing rapidly and creating a demand for professionals, there are a few other reasons to choose this role.
- High demand: With companies increasingly adopting cloud services, there’s an increase in demand for professionals with AWS experience.
- Lucrative salary: Due to the specialized work, the salaries for this field are quite lucrative.
- Career opportunities: Being an AWS data engineer opens up channels for career options like cloud solutions architect, machine learning engineer, data scientist, data architect, and more.
- Innovation: You’ll have the chance to work with upcoming and cutting-edge technologies. Plus, you work on innovative data engineering solutions.
- Continuous learning: With the field continuously evolving, you can keep learning and growing.
What’s the Demand for AWS Data Engineers?
There’s been a massive growth of data engineers in the past few years. From 2021 to 2022, the growth of data engineering has surpassed data scientists by 100%. Data engineers have the 4th highest volume of job postings compared to several other tech jobs. This clearly shows the demand for data engineers in today’s job market.
As long as businesses use data to answer business questions or drive decision-making, there will be a constant demand for data engineers. So, if you’re interested in a data engineer career, there’s no better time than now.
AWS Data Engineer Salary Details for the Top 5 Countries
Every country has differences in cost of living, demand, and skill levels, which impact AWS data engineers' salaries. Your individual certifications, experience, education, and more are added to this. However, here’s an approximate salary range to give you an idea of what to expect.
- The United States of America: Depending on your location and experience, the salary varies. A ballpark figure is a low of $82,053 to a high of $185,301. In bigger cities like San Francisco, you can expect a salary of $154,757 versus smaller cities like Atlanta, where it’s $110,774.
- The United Kingdom: The same applies in the United Kingdom as well - where the salary varies based on location and experience. The low is around £43K, and the high is around £80K. In London, the salary you can expect is £65,001 versus £49,650 in Manchester.
- Canada: The average salary in Canada for a data engineer is $98,571. In Toronto, you can expect to get paid $168,354 whereas $93,828 in Calgary.
- Australia: The average salary in Australia for a data engineer is $128,970. Cities like Sydney offer higher pay in the range of $143,929, and in Perth, it is $120,158.
- India: 10% of Indian salaries fall in the range of 394k. The median salary is ₹881k and 90% get ₹2m.
Ending of AWS Data Engineer Roadmap
From start to finish, the roadmap to becoming a data engineer is not easy, but ultimately, it’s highly rewarding. Once you are through with it, it opens up many opportunities in the field, leading to lucrative pay.
No matter what happens, remember you have acquired some knowledge and skills which you can use at some time or another. And with the opportunities that lie ahead with cloud computing combined with KnowledgeHut best Cloud Computing course, you’ve got a lot to experience and achieve.
