Amazon.com Services LLC
Data Science, Data Engineering, international stores
DataEngineerII
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Engineer II at Amazon.com Services LLC. Skills: Data engineering, Data modeling, ETL pipelines, Distributed systems. Design logical data models. Build logical data models”
What You'll Achieve.
Drive measurable productivity gains
Industry & Context.
Solve complex data challenges; Resolve root causes of complex problems
What They're Looking For.
Must Have
3+ years data engineering experience, 4+ years SQL experience, Experience with data modeling, Experience with warehousing, Experience building ETL pipelines, Experience with Python, Experience with Java, Experience with Scala, Proven ability to work with large-scale datasets, Proven ability to optimize performance
Nice to Have
Experience with Redshift, Experience with S3, Experience with AWS Glue, Experience with EMR, Experience with Kinesis, Experience with FireHose, Experience with Lambda, Experience with IAM roles, Experience with permissions, Knowledge of non-relational databases, Bachelor's degree in Computer Science, Bachelor's degree in Engineering, Bachelor's degree in related technical field, Experience with Spark, Experience with Hadoop, Experience with Hive, Experience with real-time data architectures, Experience with streaming data architectures, Familiarity with CI/CD pipelines, Familiarity with infrastructure-as-code
What You'll Do.
Design logical data models
Build logical data models
Optimize logical data models
Design data pipelines
Optimize data pipelines
Create self-service access to datasets
Work on data architecture
Collaborate to design data solutions
Write maintainable code
Optimize resource usage
Optimize system hardware
Optimize data storage
Optimize query performance
Optimize AWS infrastructure
Participate in code reviews
Participate in design discussions
Participate in team planning
Mentor junior team members
Train junior team members
Resolve root causes of complex problems
Balance customer requirements
Stay current on distributed systems technologies
How You'll Work.
Team & Collaboration
Cross-functional engineering teams; Code reviews; Design discussions; Team planning
Full Job Description
Join the Amazon Payments Products - North America Credit (APD-NAC) Data Engineering team, where we build the data infrastructure powering Amazon's credit card businesses. Our team delivers data-driven solutions that impact millions of customers across Amazon's payment ecosystem. We're a high-performing team that consistently delivers while maintaining zero-defect standards through rigorous engineering practices. We're at the forefront of AI-first transformation, implementing cutting-edge automation solutions and next-generation data architectures that drive measurable productivity gains. Our work spans above and beyond the critical business areas such as customer acquisition, fraud detection, product recommendations, financial and executive reporting. As a Data Engineer II, you'll work on high-impact projects (targeting millions of customers), large-scale customer acquisition initiatives, and fraud detection platforms. You'll collaborate with Product Managers, Data Scientists, Business Intelligence Engineers, UX Designers, Software Development Engineers, GMs, and other cross-functional engineering teams to solve complex data challenges at massive scale while driving innovation through AI-powered automation. Key job responsibilities Design, build, and optimize logical data models and data pipelines for complex datasets Own ongoing data quality and create self-service access to datasets for business intelligence Work on major portions of existing or new data architecture within the team Collaborate with Software Development Engineers and other Data Engineers to design stable, performant data solutions Write secure, stable, testable, and maintainable code with minimal defects Apply appropriate data design approaches and make judicious trade-offs without over-engineering Optimize resource usage including system hardware, data storage, query optimization, and AWS infrastructure Participate actively in code reviews, design discussions, and team planning Mentor and train j
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