Sr. AI Platform Data Engineer, Ring Decision Science, Ring Decision Science

Sr. AI Platform Data Engineer, Ring Decision Science

We seek an AI Platform Builder—a Data Engineer focused on developing platforms and agentic AI solutions—who embraces prompt-driven development with strong technical, analytical, communication, and stakeholder management skills. This role sits at the intersection of data engineering, business intelligence, and platform engineering, requiring partnership with software development engineers, scientists, data analysts, and business stakeholders across various verticals. You will design, evangelize, and implement platform features and curated datasets that power Artificial Intelligence/Machine Learning (AI/ML) initiatives and self‑service analytics, helping us provide a great neighbor experience at greater velocity.

• Build and maintain efficient, scalable, and privacy/security‑compliant data pipelines, curated datasets for AI/ML consumption, and AI‑native self‑service data platforms using an AI‑first development methodology.
• Lead AI‑assisted stakeholder engagement sessions across verticals such as Subscriptions, Security, Sales, and Marketing.
• Design and build curated datasets leveraging AI code generation and Agentic AI tools.
• Build and maintain data pipelines using AI‑assisted development with AWS services and internal Amazon tools.
• Implement AI‑powered self‑service platforms with natural language interfaces.
• Create intelligent governance systems for data classification, PII detection, and lineage tracking.
• Facilitate AI‑augmented workshops for stakeholders to explore data capabilities collaboratively.

The Analytics & Science team for Decision Sciences is at the forefront of Ring’s transformation into an AI‑powered organization. We address cross‑organizational data models, develop governance frameworks, provide direct Business Intelligence (BI) support across multiple teams, and build customer‑facing and internal AI tools that fundamentally improve how effectively and quickly the organization makes decisions.

• Experience mentoring team members on best practices.
• 5+ years of data engineering experience.
• 3+ years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab).
• Experience developing, deploying and managing AI products at scale.
• Experience with data modeling, warehousing and building Extract, Transform, and Load (ETL) pipelines for both analytics and ML use cases.
• Experience building datasets or features for machine learning models or self‑service analytics.
• Extensive hands‑on experience with Generative AI (GenAI) enhanced development pipelines, AI coding assistants, and prompt engineering.
• Demonstrated ability to build tools, frameworks, or platforms that enable others.

• Experience operating large data warehouses.
• Experience with non‑relational databases / data stores (object storage, document or key‑value stores, graph databases, column‑family databases).
• Experience with AWS technologies such as Redshift, S3, AWS Glue, EMR, Kinesis, Fire Hose, Lambda, and IAM roles and permissions.
• Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS.
• Experience building multi‑agent systems, Lang Chain/Lang Graph applications, or custom AI agent frameworks.
• Experience with prompt engineering, Retrieval‑Augmented Generation (RAG) systems, and Large Language Model (LLM) fine‑tuning.
• Experience with BI tools (Quick Sight, Tableau, Looker) and designing datasets for analytical consumption.
• Experience building or contributing to AI‑native self‑service data platforms, feature stores, or intelligent data cataloging systems.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.