Data Engineer

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Bachelor's degree
- 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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As a Data Engineer II on the Data Engineering team, you will own and drive the design of scalable data systems that power Amazon's pricing and catalog optimization. You'll lead technical initiatives at the intersection of data engineering and AI, architecting solutions that process large-scale data across Amazon's global operations. You'll mentor junior engineers, influence technical direction, and deliver high-impact data products with measurable business outcomes.


Key job responsibilities
AI-Native Infrastructure & Real-Time Processing
· Design and architect AI-native infrastructure supporting real-time data processing for AI/ML inference, training, and continuous learning at scale
· Lead the development of semantic layers and knowledge graphs enabling intelligent query routing and context-aware data access across the organization
· Architect infrastructure for agentic AI systems with multi-agent orchestration, defining patterns and best practices for the team
· Drive GenAI-powered data quality, entity resolution, and metadata management strategies that raise the bar for data integrity

Data-as-a-Product Delivery
· Own end-to-end accountability for complex data products from ingestion to consumption, defining SLAs and driving adoption across stakeholders
· Lead the delivery of data products with measurable quality metrics, customer satisfaction targets, and continuous improvement mechanisms
· Design and build self-service platforms with embedded governance, lineage, and discovery — enabling teams to independently access and trust data
· Define data contracts and API standards for reliable, versioned data consumption across downstream consumers

AWS Infrastructure & Pipeline Engineering
· Architect and optimize AWS infrastructure: EC2, Lambda, S3, Redshift, EMR — balancing performance, reliability, and cost
· Design high-throughput, fault-tolerant pipelines supporting analysts, data scientists, and AI agents at global scale
· Lead implementation of CDC and event-driven architectures for sub-minute data availability with end-to-end observability
· Drive infrastructure-as-code best practices using CDK, establishing reusable patterns and deployment standards

Technical Leadership & Operational Excellence
· Mentor Data Engineer I team members, conducting code reviews and elevating engineering standards
· Own operational health of critical pipelines, driving root cause analysis and long-term prevention strategies
· Author and maintain technical design documents, influencing architectural decisions across the team
· Lead peak readiness efforts and incident response, ensuring system reliability during high-traffic events
· Drive automation initiatives that reduce operational toil and enable non-linear scaling