Data Engineer Ii, Aws Field Experience - Investments

Amazon Amazon · Big Tech · Seattle, WA · Operations, IT, & Support Engineering

Data Engineer II role at AWS Field Experience - Investments, focusing on designing, building, and evolving data capabilities for the Investments Platform. The role involves end-to-end data solutions from ingestion to insight generation, with a significant emphasis on incorporating generative AI practices using AWS GenAI services like Bedrock, Nova, and Amazon Q to enhance efficiency, automation, and decision-making. Responsibilities include building data pipelines, data models, and analytics workflows, while also leveraging GenAI for prototyping intelligent features and automating data processes.

What you'd actually do

  1. Design and implement robust, scalable data pipelines and ETL processes using AWS-native services (e.g., Glue, Lambda, EMR, Kinesis, S3, Redshift/Spectrum).
  2. Build and maintain data models, schemas, and storage solutions across relational (SQL) and NoSQL databases, data lakes, and warehouses.
  3. Develop, automate, and optimize metrics, reports, dashboards, and analytics workflows to drive business insights and data-informed decisions.
  4. Leverage Amazon Bedrock, Nova models, Amazon Q, Kiro, and other internal AWS GenAI services to prototype intelligent features, automate data workflows, enhance data quality, and accelerate insight delivery.
  5. Champion best practices in data integrity, testing, validation, monitoring, and documentation in a fast-paced environment.

Skills

Required

  • Data pipeline design and implementation
  • ETL processes
  • AWS-native services (Glue, Lambda, EMR, Kinesis, S3, Redshift/Spectrum)
  • Data modeling and schema design
  • Relational (SQL) and NoSQL databases
  • Data lakes and warehouses
  • Metrics, reports, and dashboard development
  • Analytics workflow development
  • Data processing infrastructure management
  • Performance tuning
  • Cost optimization
  • Architectural evolution
  • Generative AI practices
  • Prototyping and proof-of-concepts
  • Automation tooling
  • Data collection, processing, and analytics
  • Data ingestion and transformation
  • AWS big data technologies
  • Data integrity best practices
  • Testing and validation
  • Monitoring
  • Documentation

Nice to have

  • Experience with Amazon Bedrock, Nova models, Amazon Q, Kiro
  • Understanding of the broader GenAI ecosystem

Other signals

  • Leverage Amazon Bedrock, Nova models, Amazon Q, Kiro, and other internal AWS GenAI services to prototype intelligent features, automate data workflows, enhance data quality, and accelerate insight delivery.
  • Demonstrate strong understanding of the broader GenAI ecosystem and apply it thoughtfully to real-world data engineering challenges in daily projects.
  • Conduct rapid prototyping, proof-of-concepts, and automation tooling to benchmark, validate, and improve data collection, processing, and analytics.