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AI Platform Data Engineer II, Ring Decision Science
2 weeks ago
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
This Role will:
* 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
* 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:
- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Knowledge of distributed systems as it pertains to data storage and computing
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
- 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
Preferred Qualifications:
- 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)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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