Amazon.com Services LLC
Technology
SeniorDataEngineer,InfrastructureReliability
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“Senior Data Engineer, Infrastructure Reliability at Amazon.com Services LLC. Skills: Data engineering, ETL pipelines, Data warehousing, Machine learning. Design scalable ETL/ELT pipelines. Build scalable ETL/ELT pipelines”
What You'll Achieve.
Keep Amazon's global fulfillment network running continuously; Move toward fully autonomous operations; Keep Amazon's fulfillment network running
Industry & Context.
Root cause analysis; Troubleshooting; Data-driven decision making
What They're Looking For.
Must Have
5+ years data engineering experience, Experience with data modeling, Experience building ETL pipelines, Experience with SQL, Experience in scripting or programming language, Experience mentoring team members, Experience building data products incrementally, Experience integrating and managing data sets, Bachelor's degree in computer science, Bachelor's degree in engineering, Bachelor's degree in analytics, Bachelor's degree in mathematics, Bachelor's degree in statistics, Bachelor's degree in IT
Nice to Have
Experience with Hadoop, Experience with Hive, Experience with Spark, Experience with EMR, Experience operating large data warehouses, Master's degree in computer science, Master's degree in engineering, Master's degree in analytics, Master's degree in mathematics, Master's degree in statistics, Master's degree in IT
What You'll Do.
Design scalable ETL/ELT pipelines
Build scalable ETL/ELT pipelines
Maintain scalable ETL/ELT pipelines
Partner with data scientists
Partner with software engineers
Partner with product managers
Define data requirements
Validate feature engineering approaches
Ensure model-ready data pipelines are reliable
Ensure model-ready data pipelines are low-latency
Build large-scale data warehouse solutions
Operate large-scale data warehouse solutions
Establish data quality frameworks
Enforce data quality frameworks
Define data governance standards
Implement data governance standards
Define access patterns
Implement access patterns
Implement documentation
Mentor junior data engineers
Eliminate bottlenecks
Improve pipeline performance
Improve pipeline cost efficiency
Improve pipeline maintainability
How You'll Work.
Team & Collaboration
Cross-domain fulfillment systems; Data scientists; Software engineers; Product managers; Robotics organization; Cross-functional teams
Communication Scope
Design review
Process & Methodology
Roadmap planning
Full Job Description
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. Key job 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
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