Sr. AI Platform Data Engineer, Ring Decision Science

Job Description

We seek an AI Platform Builder-a Data Engineer focused on developing Platforms and Agentic AI solution-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.

This role requires a first-principles approach to leveraging AI at every layer of the data stack-from using AI agents to write and optimize code, to building AI-powered platforms that serve AI models, to deploying intelligent agents that make data accessible. You will use AI to build AI infrastructure, automate the automation, and create self-improving systems that continuously enhance data quality, discoverability, and usability.

Key job responsibilities
You will 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. As a trusted technical partner to business stakeholders and data science teams, you'll deliver well-modeled, easily discoverable data optimized for specific use cases while leveraging AI-powered solutions and agentic frameworks to build continuously improving systems.

A day in the life
Lead AI-assisted stakeholder engagement sessions across verticals like 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
This Role will:
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

About the team
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.

BASIC QUALIFICATIONS - 5+ years of data engineering experience
- Experience with data modeling, warehousing and building Extract, Transform, and Load (ETL) pipelines for both analytics and ML use cases
- Experience with Structured Query Language (SQL) and at least one programming language (Python, Java, Scala, or NodeJS)
- 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 track record of building AI agents, agentic workflows, or AI-powered automation tools
- Demonstrated ability to build tools, frameworks, or platforms that enable others
PREFERRED QUALIFICATIONS - Experience with AWS technologies like Bedrock, SageMaker, Redshift, Simple Storage Service (S3), AWS Glue, EMR, Athena, Kinesis, FireHose, Lambda, Step Functions, SageMaker Feature Store, and Identity and Access Management (IAM) roles and permissions
- Experience building multi-agent systems, LangChain/LangGraph applications, or custom AI agent frameworks
- Experience with prompt engineering, Retrieval-Augmented Generation (RAG) systems, and Large Language Model (LLM) fine-tuning
- Experience in at least one modern scripting or programming language with production-quality code standards
- Experience with non-relational databases / data stores
- Experience with BI tools (QuickSight, 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

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