Amazon

Data Engineer

Seattle, WA
USD 132k - 179k
Python Java Scala Node.js SQL AWS Redshift S3 AWS Glue EMR Kinesis Firehose Lambda IAM ETL ELT Data Modeling
Description
- 3+ years of data engineering experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- 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)
- Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
- Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.



USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually
Are you looking for a role where you have the opportunity to shape Amazon’s Pricing and Promotions using data, analytics, and insights? If so, our role in Amazon's Pricing Eligibility Services is for you.

Amazon's Pricing Eligibility Services organization is seeking a highly engineering-driven Data Engineer. In this role, you will develop, build, and deliver automated data engineering infrastructure, which will deliver the best prices and experiences to our customers. Data is at the core of Amazon’s culture, and your work will have a direct impact on decision making and strategy for the Pricing organization.

This role requires a self-starting leader with high judgment that thrives in ambiguity. We are looking for a person with broad and deep data engineering skills, that is customer and delivery-focused, and a has a track record of earning your customers and peer’s trust. To be successful in this role, you will need strong cloud and data engineering skills as well as an entrepreneurial mindset.

Key job responsibilities
Lead data engineering for your business and its customers.

Develop, build, and deliver data engineering infrastructure and its operations.

Conducting deep dive analyses and tuning of our engineering infrastructure and cloud architecture.

Leverage LLMs to drive customer improvements and operations efficiencies.

Producing written documentation for your team in code and in narrative format.

Continuously invent, simplify, and automate your products, tools, services, and methodologies.

Improving back-end data sources for increased accuracy and simplicity.

Recognizing and adopting best practices in data engineering.
Amazon
Amazon

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