Data Platform Engineer II - Inbound Pipelines & ML Feeds

Amazon

Bellevue (WA)

On-site

USD 144,000 - 194,000

Full time

2 days ago

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Benefits offered by this job

Health insurance

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Paid time off

Job summary

Amazon Fulfillment Technologies seeks a Software Development Engineer II to own large-scale data infrastructure for the inbound supply chain. You will design batch/streaming pipelines, data models, and processing systems turning petabytes of data into reliable datasets.

Collaborate with ML teams to productionize workflows and raise the engineering bar for data at scale. You will mentor junior engineers, participate in on-call, and help shape the data platform roadmap in a fast-paced,

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 analytics/reporting and ML workloads.
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 to accelerate software delivery.
Solid software engineering fundamentals (testing, CI/CD, code review, production operations).

Responsibilities

Independently design, build, and operate scalable 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 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, 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.