Data Engineer II, OpsTech Team, OTS Anchor Team

AI in this role

Data Engineer building scalable data platforms and pipelines to power AI agents, machine learning systems, and operational analytics at Amazon.

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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.

Basic qualifications

- 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

Preferred qualifications

- 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 https://amazon.jobs/content/en/how-we-hire/accommodations 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 https://amazon.jobs/en/benefits.

USA, TX, Austin - 132,100.00 - 178,800.00 USD annually

How we rate this

Data Engineer II, OpsTech Team, OTS Anchor Team at Amazon rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI AgentsAI AutomationData EngineeringETLData PipelinesMachine Learning InfrastructureDistributed SystemsPython

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
Tell me about a workflow you automated with AI tools, end to end.
Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where etl was part of your work. What did you do?
Tell me about a project where data pipelines was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI Agents, AI Automation, Data Engineering, ETL, and Data Pipelines. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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