Senior Data Engineer - Amazon Web Service, ProServe Analytics and Intelligence

Amazon Web Services, Inc.; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Amazon Web Services, Inc. is seeking a Senior Data Engineer to own the governed data platform and agentic infrastructure that powers operational intelligence for ProServe.

You will architect multi-tier data warehouses, design a graph-based knowledge layer, implement MCP interfaces, and lead platform standards across the data engineering team, ensuring production-grade, secure, observable data products. This role requires systems thinking, hands‑on implementation, and mentorship of engineers

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.

Job description

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 pl