Senior Data Engineer, Infrastructure Reliability

Amazon•Nashville, TN

10d

About The Position

Join Amazon's Fulfillment Technologies & Robotics (FTR) team to build the data foundation powering the next generation of AI-enabled infrastructure reliability — a platform designed to keep Amazon's global fulfillment network running continuously, moving toward fully autonomous, zero-touch operations. As a Data Engineer III on the Infrastructure Reliability team, you will design, build, and scale the data pipelines, models, and warehousing infrastructure that feed machine learning systems, multi-agent orchestration platforms, and real-time observability tools across thousands of fulfillment sites. Your work will be operationally critical, technically motivated, and globally impactful. If you are energized by hard data problems at enormous scale — and want your work to matter in production every single day — this is the role for you. The Infrastructure Reliability team sits within Amazon's Robotics organization and operates as the cross-domain orchestration layer for a fulfillment network that processes customer orders continuously across thousands of global sites. Our mission is straightforward and non-negotiable: operations never stop, no matter what breaks. We do not own any single fulfillment domain — instead, we build the platform that sees across all of them, detecting failures that cross team boundaries and coordinating resolution faster than any single team could manage alone. We are now investing heavily in AI-powered detection, multi-agent remediation orchestration, and unified observability — moving from rule-based approaches toward LLM-powered autonomous resolution at scale. We value technical rigor, customer obsession, and hands-on depth. We are a small team working on a large and growing problem, and every team member has meaningful influence over technical direction. If you want to work on something that is technically fascinating, operationally critical, and commercially enormous, this is the team for you.

Requirements

5+ years of data engineering experience
Experience with data modeling, warehousing and building ETL pipelines
Experience with SQL
Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
Experience mentoring team members on best practices
Experience building data products incrementally and integrating and managing data sets from multiple sources
Bachelor's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent

Nice To Haves

Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience operating large data warehouses
Master's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, and serve operational data from thousands of fulfillment sites to ML models, detection systems, and dashboards
Develop and own data models that guide AI-powered progressive incident detection, consolidation, and remediation orchestration across cross-domain fulfillment systems
Partner closely with data scientists, software engineers, and product managers to define data requirements, validate feature engineering approaches, and ensure model-ready data pipelines are reliable and low-latency
Build and operate large-scale data warehouse solutions on AWS (Redshift, S3, Glue, EMR, Spark) supporting both batch and near-real-time workloads
Establish and enforce data quality frameworks, monitoring, and alerting to ensure the reliability of data feeding autonomous operational systems where data errors carry real operational risk
Define and implement data governance standards, access patterns, and documentation so that data assets are discoverable, trustworthy, and reusable across teams
Mentor junior data engineers on best practices in pipeline design, code quality, testing, and data modeling
Identify and eliminate bottlenecks in existing data infrastructure, continuously improving pipeline performance, cost efficiency, and maintainability