Amazon·E-commerce·13 days ago
13 days ago
Senior Data Engineer — Amazon Web Service, ProServe Analytics and Intelligence
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What you'll do
- Data Platform Architecture and Governance: Own the architecture and evolution of ProServe's internal data warehouse platform, including cluster topology, workload isolation, access control design, and migration sequencing at production scale.
- Foundational Data Buildout: Lead the design and buildout of canonical datasets across ProServe's core business domains.
- Pipeline Engineering and Operational Excellence: Build secure, efficient, privacy-compliant data pipelines optimized for analytics, ML, and agent consumption.
- Agentic Data Infrastructure: Build and maintain the data interfaces, including a Model Context Protocol (MCP) layer over production data systems, that enable large language models and AI agents to retrieve accurate, role-appropriate business context.
- Graph-Based Knowledge Layer: Design and build a graph-based knowledge layer (Amazon Neptune Analytics or equivalent) that enables consistent, semantic data traversal across ProServe's core business objects.
- Technical Leadership and Mentorship: Define data engineering best practices for the team, including data discovery, naming conventions, access security, and documentation standards.
What they're looking for
- 5+ years of data engineering experience
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience with MPP databases such as Amazon Redshift
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Experience in data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
Nice to have
- Experience architecting and executing large-scale data warehouse migrations, including workload isolation, cluster separation, and access control redesign
- Experience with Retrieval-Augmented Generation (RAG) systems, prompt engineering, and LLM data access patterns
- Experience with graph databases and knowledge graph construction (Amazon Neptune, Neo4j, or equivalent) for enterprise-scale semantic data applications
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Are you passionate about building the data infrastructure that powers intelligent, autonomous systems at enterprise scale? Amazon Web Services is seeking a Senior Data Engineer to own the governed data platform and agentic infrastructure that drives operational intelligence for one of AWS's fastest-growing consulting businesses.
In this role, you will architect and build the foundations that determine whether every agent answer ProServe produces is trustworthy: a governed multi-tiered data warehouse, a graph-based knowledge layer, a Model Context Protocol (MCP) interface over production data systems, and the canonical datasets that serve analytics, ML, and agentic workflows from a single source. You will lead architectural decisions, drive platform standards across the data engineering team, and translate complex technical constraints into roadmap decisions that ProServe leadership acts on.
The right candidate is an engineer who thinks in systems, not just pipelines: someone who is energized by deep technical ownership, thrives at the intersection of data architecture and AI infrastructure, and brings the rigor and judgment to make production-scale platforms trustworthy and self-improving.
Key job responsibilities
• Data Platform Architecture and Governance: Own the architecture and evolution of ProServe's internal data warehouse platform, including cluster topology, workload isolation, access control design, and migration sequencing at production scale. Define and enforce the architectural standards, data contracts, and quality gates that govern how data flows from source systems into analytics and AI consumption layers.
• Foundational Data Buildout: Lead the design and buildout of canonical datasets across ProServe's core business domains. Establish common definitions, governed relationships, metadata standards, and reusable data objects that serve analytics, ML, and agentic workflows from a single source.
• Pipeline Engineering and Operational Excellence: Build secure, efficient, privacy-compliant data pipelines optimized for analytics, ML, and agent consumption. Own monitoring, alarming, runbooks, and SLA tracking for production data infrastructure. Lead on-call rotation and drive operational health reviews across the team.
• Agentic Data Infrastructure: Build and maintain the data interfaces, including a Model Context Protocol (MCP) layer over production data systems, that enable large language models and AI agents to retrieve accurate, role-appropriate business context. Ensure these interfaces are production-grade: governed, observable, and backed by SLA-tracked refresh pipelines.
• Graph-Based Knowledge Layer: Design and build a graph-based knowledge layer (Amazon Neptune Analytics or equivalent) that enables consistent, semantic data traversal across ProServe's core business objects, supporting self-service workflows and agentic retrieval patterns that require relational context beyond what tabular data surfaces.
• Technical Leadership and Mentorship: Define data engineering best practices for the team, including data discovery, naming conventions, access security, and documentation standards. Lead design reviews across your own and adjacent team architectures. Mentor junior engineers, provide input on technical development and promotions, and build alignment across discordant architectural positions.
A day in the life
You will work at the intersection of data architecture, agentic infrastructure, and production operations. Your primary customers are the engineering and analytics teams building ProServe's production AI agents and the thousands of internal users who depend on the dashboards and self-service products those agents power. You will own the architectural decisions that define what the agentic infrastructure can and cannot do, lead design reviews, and translate complex platform constraints into roadmap decisions that ProServe leadership acts on. You will regularly collaborate with Business Intelligence Engineers, Data Engineers, and Data Scientists to push the boundaries of what is possible and drive the organization's agentic transformation.
About the team
AWS Professional Services (ProServe) partners with enterprise customers to accelerate cloud transformation across strategy, migration, modernization, and AI-driven innovation. The Analytics and Intelligence team is ProServe's internal data platform organization: the layer that transforms raw business data into trusted, governed information that field practitioners, operational leaders, and AI agents act on.
About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience with MPP databases such as Amazon Redshift
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Experience in data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
- Experience with Retrieval-Augmented Generation (RAG) systems, prompt engineering, and LLM data access patterns
- Experience with graph databases and knowledge graph construction (Amazon Neptune, Neo4j, or equivalent) for enterprise-scale semantic data applications
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.
USA, TX, Austin - 154,600.00 - 209,100.00 USD annually
USA, TX, Dallas - 154,600.00 - 209,100.00 USD annually
USA, VA, Arlington - 154,600.00 - 209,100.00 USD annually
USA, WA, Seattle - 154,600.00 - 209,100.00 USD annually
In this role, you will architect and build the foundations that determine whether every agent answer ProServe produces is trustworthy: a governed multi-tiered data warehouse, a graph-based knowledge layer, a Model Context Protocol (MCP) interface over production data systems, and the canonical datasets that serve analytics, ML, and agentic workflows from a single source. You will lead architectural decisions, drive platform standards across the data engineering team, and translate complex technical constraints into roadmap decisions that ProServe leadership acts on.
The right candidate is an engineer who thinks in systems, not just pipelines: someone who is energized by deep technical ownership, thrives at the intersection of data architecture and AI infrastructure, and brings the rigor and judgment to make production-scale platforms trustworthy and self-improving.
Key job responsibilities
• Data Platform Architecture and Governance: Own the architecture and evolution of ProServe's internal data warehouse platform, including cluster topology, workload isolation, access control design, and migration sequencing at production scale. Define and enforce the architectural standards, data contracts, and quality gates that govern how data flows from source systems into analytics and AI consumption layers.
• Foundational Data Buildout: Lead the design and buildout of canonical datasets across ProServe's core business domains. Establish common definitions, governed relationships, metadata standards, and reusable data objects that serve analytics, ML, and agentic workflows from a single source.
• Pipeline Engineering and Operational Excellence: Build secure, efficient, privacy-compliant data pipelines optimized for analytics, ML, and agent consumption. Own monitoring, alarming, runbooks, and SLA tracking for production data infrastructure. Lead on-call rotation and drive operational health reviews across the team.
• Agentic Data Infrastructure: Build and maintain the data interfaces, including a Model Context Protocol (MCP) layer over production data systems, that enable large language models and AI agents to retrieve accurate, role-appropriate business context. Ensure these interfaces are production-grade: governed, observable, and backed by SLA-tracked refresh pipelines.
• Graph-Based Knowledge Layer: Design and build a graph-based knowledge layer (Amazon Neptune Analytics or equivalent) that enables consistent, semantic data traversal across ProServe's core business objects, supporting self-service workflows and agentic retrieval patterns that require relational context beyond what tabular data surfaces.
• Technical Leadership and Mentorship: Define data engineering best practices for the team, including data discovery, naming conventions, access security, and documentation standards. Lead design reviews across your own and adjacent team architectures. Mentor junior engineers, provide input on technical development and promotions, and build alignment across discordant architectural positions.
A day in the life
You will work at the intersection of data architecture, agentic infrastructure, and production operations. Your primary customers are the engineering and analytics teams building ProServe's production AI agents and the thousands of internal users who depend on the dashboards and self-service products those agents power. You will own the architectural decisions that define what the agentic infrastructure can and cannot do, lead design reviews, and translate complex platform constraints into roadmap decisions that ProServe leadership acts on. You will regularly collaborate with Business Intelligence Engineers, Data Engineers, and Data Scientists to push the boundaries of what is possible and drive the organization's agentic transformation.
About the team
AWS Professional Services (ProServe) partners with enterprise customers to accelerate cloud transformation across strategy, migration, modernization, and AI-driven innovation. The Analytics and Intelligence team is ProServe's internal data platform organization: the layer that transforms raw business data into trusted, governed information that field practitioners, operational leaders, and AI agents act on.
About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.
Basic Qualifications
- 5+ years of data engineering experience- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience with MPP databases such as Amazon Redshift
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Experience in data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
Preferred Qualifications
- Experience architecting and executing large-scale data warehouse migrations, including workload isolation, cluster separation, and access control redesign- Experience with Retrieval-Augmented Generation (RAG) systems, prompt engineering, and LLM data access patterns
- Experience with graph databases and knowledge graph construction (Amazon Neptune, Neo4j, or equivalent) for enterprise-scale semantic data applications
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 - 154,600.00 - 209,100.00 USD annually
USA, TX, Dallas - 154,600.00 - 209,100.00 USD annually
USA, VA, Arlington - 154,600.00 - 209,100.00 USD annually
USA, WA, Seattle - 154,600.00 - 209,100.00 USD annually
E-commerce
Company
AmazonE-commerce
Dallas, United States of America
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