AI Platform Data Engineer, Ring Decisions Sciences Platform

AI-First Platform Data Engineer

Ring is seeking an AI-first Platform Data Engineer who embraces a prompt driven development philosophy with strong technical, analytical, communication, and stakeholder management skills to join our team. This role sits at the intersection of data engineering, business intelligence, and platform engineering—requiring an ability to partner 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 AI/ML initiatives and self-service analytics. All helping us provide a great neighbor experience at greater velocity.

You will work in a complex data environment where you will support various use cases including self-service business reporting, production data pipelines, machine learning feature datasets, and datasets built for AI Agents. 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.

Experience with AI-powered development tools, agentic workflows, prompt engineering, ML feature engineering, automated testing frameworks, self-service analytics platforms, and intelligent data discovery tools is mandatory. Key job responsibilities include building and maintaining 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. You will act as a trusted technical partner to business stakeholders and data science teams, deeply understanding their needs and delivering well-modeled, easily discoverable data optimized for their specific use cases.

Basic qualifications include 3+ years of data engineering experience with demonstrated stakeholder management and communication skills, experience with data modeling, warehousing and building ETL pipelines for both analytics and ML use cases, experience with SQL and at least one programming language, experience building datasets or features for machine learning models or self-service analytics, extensive hands-on experience with Gen AI enhanced development pipelines, AI coding assistants, and prompt engineering, and demonstrated ability to build AI agents, agentic workflows, or AI-powered automation tools.

Preferred qualifications include experience with AWS technologies, experience building multi-agent systems, experience with prompt engineering, RAG systems, and LLM fine-tuning, experience in at least one modern scripting or programming language, experience with non-relational databases, experience with BI tools, experience building or contributing to AI-native self-service data platforms, experience with ML frameworks, experience with orchestration tools, experience with infrastructure-as-code, experience with AI-powered monitoring, observability, and anomaly detection platforms, experience with API development, microservices architecture, and AI-enhanced API generation, experience with semantic search, vector databases, and knowledge graph technologies, experience facilitating technical workshops, training sessions, or serving in customer-facing technical roles, and knowledge of CI/CD practices for data pipelines, ML models, and AI agent deployment.

A day in the life includes leading AI-assisted stakeholder engagement sessions, designing and building curated datasets, building new data ingestions and pipelines using AI-assisted development, maintaining/improving existing data pipelines using AI-powered code analysis, evaluating, piloting, and migrating to AI-native platforms and tools, building/improving/maintaining AI-powered self-service platforms, creating intelligent governance and observability systems, and developing APIs and automation frameworks that are themselves AI-enhanced.

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 BI support across multiple teams, and build customer-facing and internal AI tools that fundamentally improve how effectively and quickly the organization makes decisions. Most critically, we are pioneering the infrastructure, standards, and approaches to make data truly useful and accessible across Ring through radical AI adoption and AI-native architectures. We recognize that data requires precise governance, rich context, and intelligent agents to unlock its full potential. This team leads the charge in developing these capabilities—starting with core datasets and scaling across the entire organization. We're not just building data pipelines; we're building self-improving, AI-powered systems that make data effortlessly accessible and continuously more valuable.