Data Engineer II, Ring/Blink Customer Service Engineering and Insights

We are seeking a Data Engineer to join our Customer Service Engineering and Insights team at Ring & Blink. This role owns the data platform foundations that enable analytics, AI initiatives, and future data-driven capabilities across the organization. You will operate and evolve ETL pipelines and AWS data infrastructure, ensuring data is secure, reliable, compliant, and ready for downstream consumption.

This role is hands-on and execution-focused, with strong emphasis on orchestration reliability, and pipeline quality. You will work closely with BI Engineers, Technical Architects, Salesforce System Developers, and platform partners to translate architecture into production-ready systems.

The role requires a high degree of ownership and self-sufficiency. The ideal candidate is comfortable independently exploring existing systems, quickly diagnosing issues, and driving improvements from problem definition through production. You will be expected to move fast in a dynamic environment, proactively identify opportunities to improve platform reliability and maintainability, and remove technical roadblocks to keep delivery moving.

Key job responsibilities

• Own and operate ETL pipelines and data infrastructure that provide data readiness for analytics, AI initiatives, and other downstream use cases
• Implement, maintain, and own data access and data-related permissions across AWS accounts, including IAM roles, trust relationships, and permissions for Athena, Redshift, and related services; debug and resolve IAM, Lake Formation, and cross-account data access issues, and coordinate with partner teams when broader AWS issues impact data pipelines
• Serve as the primary operational owner of the existing Airflow orchestration layer, focusing on execution monitoring, failure triage, alerting, SLA adherence, and pipeline reliability, while enabling BI Engineers to troubleshoot and contribute as needed
• Develop, operate, and support Python-based ETL jobs, including Airflow-executed Python tasks and jobs running on EC2 or other managed compute, ensuring reliability, observability, and maintainability through established engineering best practices
• Lead the identification and reduction of technical debt across ETL pipelines and orchestration, including evaluating job dependencies, brittle workflows, and re-run/backfill patterns, and driving incremental improvements in partnership with the team as the platform stabilizes
• Own data pipeline design considerations related to legal, security, and compliance requirements, including evaluating existing data retention and access-control implementations and redesigning or refactoring pipeline logic as needed to address gaps and ensure ongoing compliance with approved governance standards
• Partner with BI Engineers to ensure data outputs are consistent, validated, and ready for consumption, without owning business metrics or dashboards
• Create and maintain runbooks, operational documentation, and standards to support long-term scalability, knowledge transfer, and on-call readiness
• Proactively identify risks and improvement opportunities, and independently drive scoped initiatives from investigation through production, escalating architectural concerns when appropriate

A day in the life

You triage failed Airflow pipelines, debug cross-account IAM and Lake Formation issues, and refactor Python ETL jobs to reduce technical debt. You sync with BI Engineers on data requirements, update runbooks for operational clarity, and review pull requests. You monitor SLAs, ensure compliance with data governance standards, and hand off issues to on-call with clear context.

About the team

The Ring/Blink Customer Service Engineering and Insights team owns the technical side of customer service, the systems, CRM platforms, contact center tooling, integrations, data warehouse, and analytics that customer service runs on. We bring together Salesforce developers, SDEs, SAs, Business Intelligence Engineers, and Data Engineers to build and operate that stack. This role sits on the Customer Service Data and Analytics sub-team, which owns the data warehouse (Radius on Amazon Redshift), the analytics stack (QuickSight, with Tableau retiring), and the pipelines feeding both. Our culture emphasizes rapid prototyping, problem-solving, and AI-assisted development.