Amazon
Senior Data Engineer, Applied AI Solutions
$155K–$209K
Seattle, WA · Senior · Full-time · Posted Sep 24, 2026
- Experience
- 7+ years of data engineering experience; a responsibility also specifies 5+ years building and operating production pipelines and warehouses, and 5+ years of Python (or Scala/Java) and advanced SQL.
- Education
- Preferred: Bachelor's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent.
About the job
Designs and operates data infrastructure and pipelines that serve analysts, machine-learning systems, and autonomous AI agents.
What you'll do
- Build and maintain production data pipelines, warehouses, and data models for analytical, operational, and AI workloads.
- Implement data quality, lineage, monitoring, and observability, including validation, freshness and anomaly monitoring, and alerting.
- Collaborate across teams and mentor engineers on data engineering best practices.
What they ask for
- Experience with data modeling, warehousing, and ETL pipelines; SQL; and a modern programming or scripting language such as Python, Java, Scala, or NodeJS.
- Experience with MPP databases such as Amazon Redshift and highly available, distributed data extraction, ingestion, and processing systems.
- Experience building AI-serving data infrastructure, GenAI retrieval patterns, and ML/GenAI datasets and feature pipelines.
- Experience implementing data quality, lineage, and observability for AI workloads.
Nice to have
- Experience with Hadoop, Hive, Spark, or EMR; operating large data warehouses.
- Bachelor's degree in a relevant field or equivalent; distributed-systems knowledge and technical leadership experience.
Pay
154,600.00 - 209,100.00 USD annually (base salary)
Tools
Python, Scala, Java, SQL, Amazon SageMaker, Bedrock, Amazon Redshift, AWS Glue
AI focus
Build data infrastructure for AI systems and autonomous agents; support GenAI retrieval-augmented generation with chunking, embeddings, and vector stores, and maintain datasets and feature pipelines for ML/GenAI training, fine-tuning, and inference.
Model-written summary · the original posting is authoritative