Amazon.com Services LLC
Business Intelligence, Data Engineering, operations
DataEngineerII,OTS-DataANCHORTeam
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Engineer II, OTS - Data ANCHOR Team at Amazon.com Services LLC. Skills: Data engineering, Data infrastructure, AI/ML data infrastructure, ETL/ELT pipelines. Design production-grade ETL/ELT pipelines. Build production-grade ETL/ELT pipelines”
What You'll Achieve.
Deliver data products; Drive measurable business outcomes
Industry & Context.
Data-driven decision making
What They're Looking For.
Must Have
3+ years of data engineering experience, 3+ years developing and operating large-scale data structures for BI analytics using ETL/ELT, 1+ years developing and operating large-scale data structures for BI analytics using SQL, Experience with data modeling, Experience with data warehousing, Experience building ETL pipelines, Experience with SQL, Experience with Python scripting, Experience in software development, Experience with data infrastructures, Bachelor's degree in Computer Science, Bachelor's degree in Computer Engineering, Bachelor's degree in Information Technology, Bachelor's degree in related fields
Nice to Have
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, Master's degree in equivalent
What You'll Do.
Design production-grade ETL/ELT pipelines
Build production-grade ETL/ELT pipelines
Maintain production-grade ETL/ELT pipelines
Design big data infrastructure
Build big data infrastructure
Maintain big data infrastructure
Build feature engineering workflows
Build ML-ready data pipelines
Contribute to data governance
Contribute to quality standards
Support implementation of GenAI solutions
Build semantic layers
Maintain semantic layers
Build dashboard data models
Maintain dashboard data models
Follow best practices for data engineering
Contribute to best practices for data engineering
How You'll Work.
Team & Collaboration
Solution Architects; Data Engineers; Applied Scientists; BIEs; Program Managers; BI teams; ML Engineers; Data Scientists; Operational stakeholders
Full Job Description
Join the OTS Data ANCHOR team to build strategic data infrastructure powering Amazon's Operations Technology ecosystem. Our team provides critical data infrastructure support for OpsTech IT, supporting Amazon's global customer commitment. You'll work at the intersection of large-scale data processing and real-world operational impact — creating intelligence that directly influences how Amazon fulfills millions of orders across fulfillment centers, Amazon Fresh, Prime Now, Lockers, Pantry, and Amazon Campus. As a Data Engineer, you will build and maintain scalable data pipelines and AI/ML-ready data infrastructure that power AI-driven operational insights and Data Science initiatives across Amazon's global fulfillment and maintenance networks. You will design and implement ETL/ELT pipelines, build feature engineering workflows, and collaborate with Solution Architects, Data Engineers, Applied Scientists, and BIEs to deliver data products that drive measurable business outcomes. You will contribute to Data Ops and AI intelligent data practices — including data versioning, pipeline monitoring, and model retraining data support — and help establish engineering best practices within the team. This role directly enables the team's mission to implement GenAI solutions for automated reporting, diagnostics, and predictive and prescriptive analytics across worldwide operations. This is a high-impact individual contributor role with significant opportunity to grow technical scope and organizational influence at the intersection of data engineering, Data Science, and AI. Key job responsibilities - Design, build, and maintain production-grade ETL/ELT pipelines and big data infrastructure supporting OTS operational intelligence. - Build feature engineering workflows and ML-ready data pipelines that support Data Science experimentation and production model serving. - Contribute to data governance and quality standards across analytical and ML data products. - Support implementatio
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