Description

We are seeking a Data Engineer to own and operate ETL pipelines and data infrastructure supporting analytics, AI initiatives, and downstream use cases. You will manage AWS permissions across IAM, Athena, Redshift, and Lake Formation, and serve as the primary owner of the Airflow orchestration layer. Responsibilities include developing Python-based ETL jobs, reducing technical debt, ensuring legal and security compliance, partnering with BI Engineers and Data Scientists to deliver validated data, and maintaining operational documentation to drive reliability and scalability.

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
• Proactively identify risks and improvement opportunities, and independently drive scoped initiatives from investigation through production, escalating architectural concerns when appropriate

About the team
The Data Intelligence team is a new function within Amazon Customer Service. We own the end-to-end process of defining, building, implementing, and monitoring a comprehensive data strategy. We also develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences.