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- Tasks: Join the AWS Data Lake team to design and manage large datasets.
- Company: Amazon Web Services is a leader in cloud computing and data innovation.
- Benefits: Enjoy a diverse workplace, flexible work options, and opportunities for growth.
- Why this job: Be part of a cutting-edge team transforming the data warehouse industry.
- Qualifications: 3+ years in data engineering with experience in ETL and data modelling.
- Other info: Inclusive culture that supports diverse backgrounds and abilities.
The predicted salary is between 36000 - 60000 £ per year.
Data Engineer, Amazon Web Services, AWS Data Platform
AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
Each day, thousands of developers make trillions of transactions worldwide on our cloud. Almost all of them are harnessing the power of Amazon Web Services (AWS) to enable innovative applications, websites, and businesses. We store all these transactions for analysis and reporting.
Amazon Web Services is seeking a Data Engineer to join the AWS Data Lake team. Amazon.com has a culture of data-driven decision-making, and demands business intelligence that is timely, accurate, and actionable.
The AWS Data Platform team\’s mission is to help customers to see and understand their use of the AWS Cloud. We collect and process billions of usage transactions every day into actionable information in the Data Lake and make it available to our internal service owners to analyze their business and serve our external customers.
We are truly leading the way to disrupt the data warehouse industry. We are accomplishing this vision by leveraging relational database technologies like Redshift along with emerging Big Data technologies like Elastic Map Reduce (EMR) to build a data platform capable of scaling with the ever-increasing volume of data produced by AWS services. The successful candidate will shape and build AWS\’ data lake and supporting systems for years to come.
You should have deep expertise in the design, creation, management, and business use of large datasets, across a variety of data platforms. You should have excellent business and communication skills to work with business owners to understand data requirements and to build ETL to ingest the data into the data lake. You should be an expert at designing, implementing, and operating stable, scalable, low-cost solutions to flow data from production systems into the data lake. Above all, be passionate about working with vast data sets and someone who loves to bring datasets together to answer business questions and drive growth.
BASIC QUALIFICATIONS
– 3+ years of data engineering experience
– Experience with data modeling, warehousing and building ETL pipelines
PREFERRED QUALIFICATIONS
– Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
– Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit this link for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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Data Engineer, Amazon Web Services, AWS Data Platform employer: Amazon
Contact Detail:
Amazon Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer, Amazon Web Services, AWS Data Platform
✨Tip Number 1
Familiarise yourself with AWS services, especially those mentioned in the job description like Redshift, S3, and EMR. Having hands-on experience or projects showcasing your skills with these technologies can set you apart from other candidates.
✨Tip Number 2
Network with current or former employees of AWS, particularly those in data engineering roles. They can provide insights into the company culture and the specific skills that are valued, which can help you tailor your approach during interviews.
✨Tip Number 3
Prepare to discuss your experience with ETL processes and data modelling in detail. Be ready to share specific examples of how you've built pipelines or managed large datasets, as this will demonstrate your practical knowledge and problem-solving abilities.
✨Tip Number 4
Showcase your passion for data by discussing any personal projects or contributions to open-source initiatives related to data engineering. This not only highlights your skills but also your enthusiasm for the field, which is crucial for a role at AWS.
We think you need these skills to ace Data Engineer, Amazon Web Services, AWS Data Platform
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Data Engineer position at Amazon Web Services. Familiarise yourself with key technologies mentioned in the job description, such as AWS services, data modelling, and ETL pipelines.
Tailor Your CV: Customise your CV to highlight relevant experience and skills that align with the job description. Emphasise your data engineering experience, particularly with AWS technologies like Redshift, S3, and EMR, as well as your ability to work with large datasets.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data engineering and your understanding of AWS's mission. Use specific examples from your past experiences to demonstrate how you meet the qualifications and can contribute to the AWS Data Lake team.
Proofread and Edit: Before submitting your application, carefully proofread your CV and cover letter for any spelling or grammatical errors. Ensure that your documents are clear, concise, and professional, as attention to detail is crucial in this role.
How to prepare for a job interview at Amazon
✨Showcase Your Technical Skills
Make sure to highlight your experience with AWS technologies like Redshift, S3, and EMR. Be prepared to discuss specific projects where you've built ETL pipelines or managed large datasets, as this will demonstrate your hands-on expertise.
✨Understand the Business Impact
Amazon values data-driven decision-making, so be ready to explain how your work as a Data Engineer has influenced business outcomes. Think of examples where your data solutions have led to actionable insights or improved efficiency.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities. Practice explaining your thought process when designing scalable data solutions or troubleshooting issues in data pipelines, as this will showcase your analytical skills.
✨Emphasise Communication Skills
Since you'll be working closely with business owners, it's crucial to demonstrate your communication skills. Prepare to discuss how you've collaborated with non-technical stakeholders to gather requirements and translate them into technical solutions.
