Job description

Amazon Fulfillment Technologies (AFT) Inbound builds the systems that move inventory into Amazon's fulfillment network at massive scale. We're looking for a Software Development Engineer II with deep data engineering expertise to independently own the large-scale data infrastructure that powers intelligent decision-making across the inbound supply chain. You'll design and build the batch and streaming pipelines, data models, and processing systems that turn petabytes of raw supply-chain data into reliable, well-governed datasets. You'll partner closely with Applied and Research Scientists to feed and productionize Machine Learning (ML) systems, and you'll raise the engineering bar for how the team builds and operates data at scale. Key job responsibilities • Independently design, build, and operate scalable, reliable data pipelines (batch and streaming) that ingest, transform, and serve large volumes of supply-chain data. • Own data modeling, data warehousing, and the design of curated datasets that serve analytics, reporting, and Machine Learning (ML) use cases. • Build and maintain feature engineering pipelines and feature stores that supply ML models with high-quality, low-latency data. • Lead design reviews and make sound technical trade-offs across performance, cost, scalability, and operational complexity for your area of ownership. • Own data quality, lineage, and observability across the datasets, pipelines, and services you build, and drive systemic improvements to reliability. • Partner with Applied and Research Scientists to operationalize ML workflows, including feature delivery, training data preparation, and model-scoring pipelines. • Deliver robust, well-tested, production-quality software and drive engineering best practices (code reviews, Continuous Integration/Continuous Deployment (CI/CD), monitoring, operational excellence). • Diagnose and resolve complex production and data issues; participate in on-call and reduce operational load through automation. • Mentor SDE I and early-career engineers and influence the technical roadmap for the team's data platform. About the team Amazon Fulfillment Technologies (AFT) Inbound builds the software and science that move inventory into Amazon's fulfillment network — from the moment a supplier ships product to the point it's received, stowed, and ready to fulfill customer orders. Operating at the scale of billions of units across a global network of fulfillment centers, our team owns the data infrastructure and intelligent systems that make inbound flow faster, more accurate, and more cost-efficient. We work at the intersection of large-scale data engineering and applied Machine Learning (ML), partnering closely with Applied and Research Scientists to turn petabytes of supply-chain data into decisions that ship every day. If you're excited by hard distributed-systems problems, real-world scale, and building the data foundations that power automation and optimization across Amazon's supply chain, you'll find a high-ownership, high-impact home here. - 3+ years of building complex software systems experience - Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field - Strong Python proficiency and experience with SQL at scale - Experience building and owning batch and streaming data pipelines in production environments - Experience with big data technologies (Spark, Hive, Presto, or similar distributed computing frameworks) - Experience building data models and ETL/ELT pipelines that serve both analytics/reporting and ML workloads (feature engineering, preprocessing, training data management) - Experience collaborating with science or ML teams to take models from prototype to production (training data preparation, feature delivery, model-scoring pipelines) - Experience with data quality, validation, or observability practices for production datasets - Experience using AI coding assistants or agentic development tools (e.g., Amazon Q Developer, Copilot, Kiro, or similar) to accelerate software delivery - Solid software engineering fundamentals (testing, CI/CD, code review, production operations) - Experience deploying ML models to production environments - Experience designing and optimizing large-scale data architectures (data lakes, data warehouses, lakehouse patterns) - Experience with MLOps tooling (SageMaker Pipelines, Step Functions, MLflow, or similar) for operationalizing model training, scoring, and deployment workflows - Experience partnering cross-team with SDEs to design and ship ML-integrated services or systems - Experience using agentic workflows to generate, test, and iterate on code, infrastructure, or pipeline components at scale - Experience with prompt engineering and LLM APIs - Experience with ML evaluation frameworks (especially for generative AI / LLM outputs) - Experience with data lineage, cataloging, and observability tooling at scale - Experience with streaming data processing (Kafka, Kinesis, Flink) - Experience with data orchestration tools (Airflow, Step Functions, AWS Glue workflows) - Experience with infrastructure-as-code (CDK, CloudFormation, Terraform) - Experience building metrics and reporting infrastructure consumed by business stakeholders - Background in supply-chain, logistics, fulfillment, or operational environments 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 amazon.jobs 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 amazon.jobs . USA, WA, Bellevue - 143,700.00 - 194,400.00 USD annually

Requirements

Must-haves

Key job responsibilities
Independently design, build, and operate scalable, reliable data pipelines (batch and streaming) that ingest, transform, and serve large volumes of supply-chain data.
Own data modeling, data warehousing, and the design of curated datasets that serve analytics, reporting, and Machine Learning (ML) use cases.
Build and maintain feature engineering pipelines and feature stores that supply ML models with high-quality, low-latency data.
Lead design reviews and make sound technical trade-offs across performance, cost, scalability, and operational complexity for your area of ownership.
Own data quality, lineage, and observability across the datasets, pipelines, and services you build, and drive systemic improvements to reliability.
Partner with Applied and Research Scientists to operationalize ML workflows, including feature delivery, training data preparation, and model-scoring pipelines.
Deliver robust, well-tested, production-quality software and drive engineering best practices (code reviews, Continuous Integration/Continuous Deployment (CI/CD), monitoring, operational excellence).
Diagnose and resolve complex production and data issues; participate in on-call and reduce operational load through automation.
Mentor SDE I and early-career engineers and influence the technical roadmap for the team's data platform.
About the team
3+ years of building complex software systems experience
Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field
Strong Python proficiency and experience with SQL at scale
Experience building and owning batch and streaming data pipelines in production environments
Experience with big data technologies (Spark, Hive, Presto, or similar distributed computing frameworks)
Experience building data models and ETL/ELT pipelines that serve both analytics/reporting and ML workloads (feature engineering, preprocessing, training data management)
Experience collaborating with science or ML teams to take models from prototype to production (training data preparation, feature delivery, model-scoring pipelines)
Experience with data quality, validation, or observability practices for production datasets
Experience using AI coding assistants or agentic development tools (e.g., Amazon Q Developer, Copilot, Kiro, or similar) to accelerate software delivery
Solid software engineering fundamentals (testing, CI/CD, code review, production operations)
Experience deploying ML models to production environments
Experience designing and optimizing large-scale data architectures (data lakes, data warehouses, lakehouse patterns)
Experience with MLOps tooling (SageMaker Pipelines, Step Functions, MLflow, or similar) for operationalizing model training, scoring, and deployment workflows

Nice-to-haves

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Keywords to mirror

machine learningmlci/cdpythonsqlaillmaws

Platform: amazon
Effort: medium

Benefits

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Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave