Senior Data Engineer, Applied AI Solutions

The newest business group in AWS, Applied AI Solutions are built by AWS and AWS Partners to deliver applied AI solutions that leverage Amazon’s operational expertise and that businesses love and trust for their day-to-day success. Our ambition is to become a partner which companies can rely on to run their business every day, putting AI to work delivering better customer experience, operational excellence and speed.

We are seeking a Senior Data Engineer to design, build and maintain our next-generation data infrastructure - one that seamlessly serves both human analysts and AI systems. This role sits at the intersection of traditional enterprise data warehousing and innovative AI technologies, requiring someone who can bridge these worlds to create a unified, future-proof data ecosystem.

As a key member of our data team, you'll collaborate across organizational boundaries with data scientists, engineers, analytics teams, and business stakeholders to develop innovative and scalable solutions that push the boundaries of what's possible with our data assets.

You'll be responsible for ensuring our datasets maintain the highest levels of accuracy, consistency, and observability - implementing comprehensive monitoring, lineage tracking, and self-healing mechanisms that maintain data quality at scale. Your infrastructure will support both analysts / scientists and autonomous AI agents with equal effectiveness, requiring thoughtful interfaces, documentation, and metadata that serve both audiences.

In this role, you'll champion a forward-thinking approach to data infrastructure that anticipates the evolving needs of AI systems while maintaining the reliability and performance that business operations demand. You'll help shape our technical roadmap for data systems that will serve as the foundation for our organization's AI transformation journey.

Key job responsibilities
- 5+ years of data engineering, building and operating production pipelines and warehouses.
- Experience building data infrastructure that serves AI systems and autonomous agents, not just human analysts, including machine-consumable interfaces, metadata, and documentation.
- Experience with GenAI data patterns end to end: chunking, embeddings, and vector stores for retrieval-augmented generation.
- Experience building and maintaining datasets and feature pipelines for ML/GenAI training, fine-tuning, and inference (Amazon SageMaker, Bedrock, or equivalent).
- Experience implementing data quality, lineage, and observability that AI workloads depend on including validation, freshness/anomaly monitoring, and alerting at scale.
- 5+ years of Python (or Scala/Java) and advanced SQL, including performance tuning at scale.
- Experience with batch and streaming ETL/ELT on AWS (Glue, EMR/Spark, S3, Athena) and a production cloud data warehouse (Amazon Redshift or equivalent).
- Experience designing data models and schemas for analytical, operational, and AI/retrieval workloads.
- Experience with workflow orchestration (Step Functions, Airflow, or Glue Workflows).