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.
BASIC QUALIFICATIONS
- 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)
PREFERRED QUALIFICATIONS
- 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
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, Bellevue - 143,700.00 - 194,400.00 USD annually
