Data Engineer

- 1+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Bachelor's degree
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The Data Engineer I role within the Data Platform and Analytics team is a foundational technical position responsible for building and maintaining modern data infrastructure that powers business intelligence and advanced analytics at scale. This role focuses on engineering real-time data processing systems for AI/ML workloads, delivering data-as-a-product with clear SLAs, and managing AWS-native pipeline architectures. Working at the intersection of data engineering and AI systems, the Data Engineer I creates reliable, scalable infrastructure that enables intelligent data access, automated workflows, and advanced analytics — driving actionable insights for business stakeholders.



Key job responsibilities
Modern Data Infrastructure & Real-Time Processing
• Engineer modern data infrastructure supporting real-time data processing for AI/ML inference and training workloads
• Build semantic layers enabling intelligent query routing and context-aware data access
• Develop infrastructure for AI-powered automated workflows and orchestration
• Implement AI-driven data quality, entity resolution, and metadata management solutions

Data-as-a-Product Delivery
• Own end-to-end accountability for data products from ingestion to consumption
• Deliver data products with clear SLAs, quality metrics, and customer satisfaction measures
• Build self-service platforms with embedded governance, lineage, and discovery capabilities
• Establish data contracts and APIs for reliable, versioned data consumption

AWS Infrastructure & Pipeline Engineering
• Manage AWS resources including EC2, Lambda, S3, Redshift, and EMR
• Build high-quality data pipelines supporting analysts, data scientists, and downstream consumers
• Implement CDC and event-driven architectures for real-time data availability
• Deploy infrastructure-as-code using CDK