Description

How often have you had an opportunity to be a member of a team that is tasked with solving customer needs through disruptive and innovative technology? Everyone on the team needs to be entrepreneurial, wear many hats and work in a fast-paced, ambiguous, and highly collaborative environment that’s more startup than big company. If this sounds intriguing, then we’d like to talk to you about a role on the Amazon Defect Elimination Analytics team. This team drives Amazon towards a defect-free customer experience by building technology that rapidly identifies defects, associates them with the information required to resolve the root cause, and prioritizes the multitude of improvement opportunities based on business and customer needs. To continue expanding our defect elimination program, we seek a passionate, results-oriented, Senior Data Engineer.

The Senior Data Engineer will partner with Software Developers, Research Scientists, Business Intelligence Engineers, Program & Product Managers to provide insights on customer feedback, create key performance indicators for our products, and assist in feature engineering and model development. You will also play a key role in building the data infrastructure that powers AI/ML and LLM-based systems, including designing pipelines that feed agentic workflows and retrieval-augmented generation (RAG) architectures. The ideal candidate has strong business judgment, organization skills, backbone, experience measuring product performance, and collaborates well with product owners to answer key questions. The operating environment is fast paced and dynamic, however has a strong team orinted and welcoming culture. To thrive, you must be detail oriented, enthusiastic and flexible, in return you will gain tremendous experience with the latest in big data technologies, generative AI infrastructure, and exposure to statistical and Natural Language modeling through collaboration with scientists on global issue detection models and development.

Key job responsibilities
- Design, develop and maintain scaled, automated, user-friendly systems, reports, dashboards, etc.
- Partner with operations/business teams/economist/ML teams to consult, develop and implement KPI's, automated reporting/process solutions and data infrastructure improvements to meet business needs.
- Build and maintain data infrastructure for AI agent systems, including vector databases, embedding pipelines, and retrieval-augmented generation (RAG) data stores.
- Design data architectures that enable agentic workflows - structured data access layers, tool-use APIs, context management systems that AI agents consume autonomously, self-serve analytics.
- Develop observability and evaluation pipelines for LLM-powered features, including tracking model performance, hallucination rates, latency, and cost metrics at scale.
- Apply analytic skill to extract meaningful insights and learnings from large and complicated data sets, including unstructured text corpora used for generative AI applications.
- Serve as liaison with Business and technical teams to achieve project objectives, requiring data gathering, problem solving, modeling, and communication of insights and recommendations.
- Stay current with advances in AI/ML data infrastructure (e.g., feature stores, vector search, streaming inference pipelines) and evaluate their applicability to defect elimination use cases.

A day in the life
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!