Data Engineer I, Payment Acceptance & Experience

The Payment Acceptance & Experience team is looking for a Data Engineer with a deep understanding of the full life-cycle of data generation and application. Our team is responsible for how Amazon's customers pay on Amazon's sites and through Amazon's services around the globe.

About the Role

As a Data Engineer I, you will design, develop, implement, test, and operate large-scale, high-volume, high-performance data structures for analytics, reporting, and machine learning. You will build the data foundations behind the business metrics our leaders rely on, develop the controllership signals that keep those metrics trustworthy, and help shape how Data Engineers work in a world where AI agents are part of the toolkit. You will leverage your expertise in data pipelines and persistence to build systems and tools that lower the cost of performing advanced analysis while also expanding the number and types of analyses that can be performed. You will take the lead in identifying architecture deficiencies and solving for the same by building advanced software solutions.

Key job responsibilities
- Implement real-time and batch data ingestion routines using best practices in data modeling.
- Develop ETL/ELT processes using AWS technologies and Big Data tools.
- Translate business and functional requirements into robust, scalable solutions that fit the overall data architecture.
- Build data quality and controllership checks for completeness, freshness, and lineage on the datasets behind key business metrics, and design alerting and triage paths.
- Contribute to AI-native engineering infrastructure by building scaffolding, guardrails, and observability for AI agents that automate Data Engineering workflows.
- Migrate legacy datasets onto a centralized data platform with parallel-run validation, downstream consumer coordination, and catalog updates.
- Maintain a semantic layer that maps business terms to physical data structures, so human users and AI agents understand the data they query.
- Analyze source data systems and drive best practices in source teams.
- Participate in the full development life cycle: design, implementation, testing, documentation, delivery, support, and maintenance.
- Produce comprehensive, usable dataset documentation and metadata.
- Evaluate dataset implementations and recommend new or existing software products and tools.

A day in the life
On a given day, you'll check the health of the pipelines that power our weekly business reviews. If an overnight check flagged something, say a payments signal came in late or a row count looked off, you'll pull in the source team and work the issue until the data is trustworthy again. Later you might be in a design review with a senior engineer, walking through how to migrate a legacy dataset onto our central data platform without disrupting the analytics teams that read from it.

In the afternoon, you'll switch to AI-native work. You might prototype an agent that automates a task you used to do by hand, or write the guardrails that keep an agent from making expensive mistakes. You'll partner with peers on our team and with Data Engineers in adjacent payments teams, since the patterns we build here are meant to be reusable.

Your customers are the finance, business intelligence, scientists, and product teams who depend on PAE data to understand how payments perform globally, and the leaders who use those numbers to make decisions. You'll work closely with senior Data Engineers who will coach you through ambiguity, review your designs, and help you grow quickly.

Basic Qualifications

- 2+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Experience with database, data warehouse or data lake solutions

Preferred Qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions

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 - 101,300.00 - 160,000.00 USD annually