Senior Data Engineer, Strategic Partnerships & IMPACT360

About the role

This position is part of the AWS Specialist and Partner Organization (ASP). Specialists own the end-to-market strategy for their respective technology domains, providing the business and technical expertise to help our customers succeed. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together they provide our customers with the expertise and scale needed to build innovative solutions for their most complex challenges.

Responsibilities

• Own the data architecture for your domain areas (e.g., ingestion, entity resolution, vendor relationship modeling) and contribute to broader platform architecture decisions
• Deliver with limited guidance where logical data models and end-to-end data flows are not yet defined
• Onboard and integrate disparate data sources from across Amazon (AWS, Retail, Procurement, Finance, vendor systems); resolve conflicts across inconsistent real-world data and drive cross-team alignment on data definitions and ownership
• Evolve data sourcing and modeling toward governed, reusable, directly-consumed data products; drive the migration within your domains without disrupting downstream consumers
• Build and operate pipelines across distributed processing, orchestration, and an open-lakehouse foundation on AWS (e.g., Glue/Spark, Airflow, Iceberg, Athena), governed with Lake Formation and cross-account IAM
• Raise operational excellence: incremental and retry-safe loads, atomic publication, dependency-aware scheduling, and data-quality validation
• Design data systems within your scope that serve diverse access patterns (batch analytics, governed BI, ML training datasets)
• Partner with the science team to build the data infrastructure for forecasting and reinforcement-learning work, including feature pipelines, training datasets, decision logs, and reward signals
• Drive engineering and operational excellence best practices across the data infrastructure
• Mentor and develop peers
• Make trade-offs between short-term delivery needs and long-term architectural scalability

Qualifications

• 5+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with distributed data processing using Spark (or equivalent) and workflow orchestration (e.g., Airflow)
• Strong SQL skills and proficiency in at least one scripting language (Python preferred) for data manipulation and pipeline development
• Experience designing data schemas and operating data stores in support of analytics and downstream consumers
• Experience working with cloud data services (AWS preferred)