AI Platform Data Engineer, Ring Decisions Sciences Platform

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 engineeringrequiring 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 stackfrom 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
This role will be responsible for 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. You are expected to default to AI-powered solutions leverage agentic frameworks and build systems that continuously learn and improve through AIaccelerating development velocity improving data quality and enabling stakeholder independence through intelligent automation.

Basic qualifications

* 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 (Python Java Scala or NodeJS)
* 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 (GitHub Copilot Amazon Q Cursor etc.) 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 S3 AWS Glue EMR Athena Kinesis FireHose Lambda Step Functions SageMaker Feature Store and IAM roles and permissions
* Experience building multi-agent systems LangChain/LangGraph applications or custom AI agent frameworks
* Experience with prompt engineering RAG (Retrieval Augmented Generation) systems and LLM fine-tuning
* Experience in at least one modern scripting or programming language such as Python Java Scala or NodeJS with production-quality code standards
* Experience with non-relational databases / data stores (object storage document or key-value stores graph databases vector databases column-family databases)
* 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
* Experience with ML frameworks (TensorFlow PyTorch Scikit-learn LangChain) and feature engineering best practices
* Experience with orchestration tools (Airflow Step Functions MWAA) and AI-powered workflow automation
* Experience with infrastructure-as-code (CDK Terraform CloudFormation) and AI-assisted infrastructure management
* 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
* Knowledge of CI/CD practices for data pipelines ML models and AI agent deployment

A day in the life
* Lead AI-assisted stakeholder engagement sessions using AI tools to synthesize requirements generate technical specifications and create stakeholder-ready documentation from Business Stakeholders ML Scientists and Data Scientists across verticals such as Subscriptions Security & Compliance Sales Reverse Logistics Finance Product Marketing and Customer Support
* Design and build curated datasets for analytics feature stores training datasets and inference pipelinesleveraging AI code generation AI-powered data profiling and automated feature engineering tools to accelerate delivery
* Build new data ingestions and pipelines using AI-assisted development (Amazon Q custom AI agents) with Native AWS services (Kendra Glue EMR Step Functions Lambda SageMaker) and internal Amazon toolsletting AI generate boilerplate code optimize queries and suggest architectural improvements
* Maintain/improve existing data pipelines using AI-powered code analysis automated refactoring tools and intelligent monitoring systems that proactively identify optimization opportunities and data quality issues
* Evaluate pilot and migrate to AI-native platforms and toolsreplacing traditional development workflows with agentic frameworks AI-powered orchestration and intelligent automation that continuously improves system performance
* Build/improve/maintain AI-powered self-service platforms that use natural language interfaces automated data discovery intelligent query optimization and conversational AI to enable stakeholders to independently access and consume data without technical expertise
* Create intelligent governance and observability systems that automatically classify data detect PII enforce access policies track lineage and provide AI-generated insights into system health
* Develop APIs and automation frameworks that are themselves AI-enhancedusing agents to generate API specifications create test cases optimize performance and maintain documentation

About the team
The Analytics & Science team for Decision Sciences is at the forefront of Rings 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 capabilitiesstarting with core datasets and scaling across the entire organization. Were not just building data pipelines; were building self-improving AI-powered systems that make data effortlessly accessible and continuously more valuable. We use AI to build AI infrastructure creating a flywheel of increasing intelligence and capability.


- 3 years of data engineering experience
- 4 years of SQL experience
- Experience with data modeling warehousing and building ETL pipelines

- Experience with AWS technologies like Redshift S3 AWS Glue EMR Kinesis FireHose Lambda and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage document or key-value stores graph databases column-family databases)

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