Job description

Join OpsTech to build strategic data infrastructure powering Amazon’s global operations technology ecosystem. OpsTech provides critical technology and data capabilities that support Amazon’s customer commitment worldwide. You will 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, Amazon Campus, and other operational environments. As a Data Engineer, you will build and evolve scalable data platforms that power analytics, machine learning, and AI driven experiences across Amazon’s global fulfillment and maintenance networks. You will design high performance data pipelines, create trusted data products, and work with Solution Architects, Data Engineers, Applied Scientists, and Business Intelligence Engineers to turn complex operational data into intelligence that can be used by both people and AI systems. You will help shape modern data engineering practices across OpsTech, including automated data quality, observability, lineage, data contracts, orchestration, and intelligent pipeline operations. Your work will provide the trusted data foundation behind AI agents, machine learning systems, operational analytics, and automated decision making at global scale. This is a high impact individual contributor role with significant opportunity to expand your technical scope and influence how OpsTech builds the next generation of data and AI capabilities. Key job responsibilities • Build scalable data pipelines and platforms that transform complex operational data into trusted, AI ready data products. • Power AI agents, machine learning systems, and intelligent automation with reliable data, context, and semantic layers. • Develop batch and streaming architectures that deliver timely operational signals for analytics, detection, diagnosis, and decision making. • Create reusable datasets and data products that support analytics, experimentation, production models, and operational applications. • Build feature pipelines, training datasets, and production workflows that connect data engineering with machine learning and AI. • Improve platform reliability through automated data quality, observability, lineage, testing, and anomaly detection. • Design semantic models and metrics that give operators, leaders, analysts, and AI systems a consistent understanding of the business. • Partner with Data Scientists, ML Engineers, Business Intelligence Engineers, Solution Architects, Program Managers, and operations teams to deliver measurable business impact. • Raise the engineering bar by improving scalability, maintainability, governance, and development standards across OpsTech. • Explore emerging data and AI technologies and help shape the next generation of OpsTech data platforms. A day in the life You will work closely with Data Engineers, Data Scientists, ML Engineers, Business Intelligence Engineers, Solution Architects, Program Managers, and operations teams across OpsTech. Your day may include designing a new data pipeline, reviewing architecture for an AI powered application, improving the reliability of a critical dataset, or working with partners to understand an operational problem and turn it into a scalable data solution. You will spend time building and improving data products used by analysts, operators, leaders, machine learning systems, and AI agents. You may investigate data quality issues, optimize large scale processing workflows, improve observability, or develop new semantic models that make complex operational data easier to understand and use. You will also participate in design reviews, code reviews, technical discussions, and planning sessions while owning projects from initial problem definition through production launch. The problems are varied, technically challenging, and directly connected to how Amazon operates at global scale. - 3+ years of data engineering experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience - Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field - Experience with data modeling, warehousing and building ETL pipelines - Experience with Python in a data engineering environment, including building data processing pipelines, automation, and testing - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions - Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) - Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit amazon.jobs for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. 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 amazon.jobs . USA, TX, Austin - 132,100.00 - 178,800.00 USD annually

Requirements

Must-haves

This is a high impact individual contributor role with significant opportunity to expand your technical scope and influence how OpsTech builds the next generation of data and AI capabilities.
Key job responsibilities
Build scalable data pipelines and platforms that transform complex operational data into trusted, AI ready data products.
Power AI agents, machine learning systems, and intelligent automation with reliable data, context, and semantic layers.
Develop batch and streaming architectures that deliver timely operational signals for analytics, detection, diagnosis, and decision making.
Create reusable datasets and data products that support analytics, experimentation, production models, and operational applications.
Build feature pipelines, training datasets, and production workflows that connect data engineering with machine learning and AI.
Improve platform reliability through automated data quality, observability, lineage, testing, and anomaly detection.
Design semantic models and metrics that give operators, leaders, analysts, and AI systems a consistent understanding of the business.
Partner with Data Scientists, ML Engineers, Business Intelligence Engineers, Solution Architects, Program Managers, and operations teams to deliver measurable business impact.
Raise the engineering bar by improving scalability, maintainability, governance, and development standards across OpsTech.
Explore emerging data and AI technologies and help shape the next generation of OpsTech data platforms.
A day in the life
You will work closely with Data Engineers, Data Scientists, ML Engineers, Business Intelligence Engineers, Solution Architects, Program Managers, and operations teams across OpsTech.
You will also participate in design reviews, code reviews, technical discussions, and planning sessions while owning projects from initial problem definition through production launch.
The problems are varied, technically challenging, and directly connected to how Amazon operates at global scale.
3+ years of data engineering experience
1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
Experience with data modeling, warehousing and building ETL pipelines
Experience with Python in a data engineering environment, including building data processing pipelines, automation, and testing
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningaimlpythonaws

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave