Data Engineer Ii, Creator Services

Amazon Amazon · Big Tech · Culver City, CA · Project/Program/Product Management--Non-Tech

Data Engineer II role focused on building production-grade ETL/ELT pipelines and data models to support AI-driven initiatives and self-service analytics within Amazon's Creator Services team. The role involves leveraging AWS services and collaborating with BI engineers and business stakeholders to drive monetization outcomes for creators.

What you'd actually do

  1. Design, build, and maintain production-grade ETL/ELT pipelines that process data at scale with high reliability and performance
  2. Architect and curate data models, warehouses, and dimensional schemas that serve as the foundation for self-service analytics and AI initiatives
  3. Drive end-to-end technical ownership of data platform components — from system design and architecture reviews to deployment, monitoring, and incident response
  4. Leverage AWS services — including Redshift, S3, Glue, Lambda, ECS, DynamoDB, and Bedrock — to build scalable and cost-efficient data platforms
  5. Partner with BI engineers, program managers, and business stakeholders to translate data needs into technical solutions, while advocating for data engineering best practices across the team

Skills

Required

  • 3+ years of data engineering experience
  • 3+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding experience
  • Experience with database, data warehouse or data lake solutions
  • 3+ years with at least one programming language (Python, Scala, or Java) for data processing
  • 3+ years of developing and operating large-scale data structures for business intelligence analytics

Nice to have

  • 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)
  • Experience in operational excellence, security compliance, and distributed systems resiliency
  • Strong foundation in data governance, access controls, and compliance standards (e.g., IAM least-privilege, data classification, security certifications)
  • Demonstrated ability to provide technical leadership and advocating for data engineering best practices across cross-functional teams
  • Skilled at designing and building internal tools, APIs, or software applications that serve data products or automate business processes
  • Experience applying AI/ML technologies (e.g., large language models, generative AI, or predictive modeling) to data engineering or automation use cases

What the JD emphasized

  • AI-driven initiatives
  • data engineering best practices

Other signals

  • AI-driven initiatives
  • data engineering best practices
  • production-grade ETL/ELT pipelines