Amazon Web Services (AWS) is looking for a Senior Data Engineer to join the Strategic Initiatives team, which focuses on managing strategic partnerships across Amazon. This role involves owning the data architecture for an internal deal intelligence platform that integrates various data sources to support decision-making. The position requires collaboration across multiple teams and emphasizes the evolution of data sourcing and modeling towards reusable data products. The ideal candidate will thrive in a dynamic environment and contribute to the growth initiatives of Amazon's businesses.

• 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 such as Airflow
• Strong SQL skills and proficiency in at least one scripting language, preferably Python
• Experience designing data schemas and operating data stores in support of analytics and downstream consumers
• Experience working with cloud data services, preferably AWS
• Experience with training and deploying machine learning systems or data infrastructures such as relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda
• Experience with open table formats like Iceberg, Hudi, or Delta Lake and lakehouse architectures on a cloud object store
• Hands-on experience with the AWS data stack including Glue, Athena, Lake Formation, S3, and IAM
• Experience with entity resolution and building unified records from inconsistent multi-source data
• Experience partnering with science or ML teams to productionize data for model training and inference
• Experience onboarding and integrating data from disparate source systems and driving cross-team alignment on data definitions
• Experience modernizing or re-architecting legacy data pipelines toward reusable, governed data products
• Track record of mentoring engineers and driving operational excellence

• Own the data architecture for your domain areas 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 and resolve conflicts across inconsistent real-world data.
• Evolve data sourcing and modeling toward governed, reusable, directly-consumed data products.
• Build and operate pipelines across distributed processing, orchestration, and an open-lakehouse foundation on AWS.
• Raise operational excellence through incremental and retry-safe loads, atomic publication, and data-quality validation.
• Design data systems that serve diverse access patterns including batch analytics and ML training datasets.
• Partner with the science team to build the data infrastructure for forecasting and reinforcement-learning initiatives.
• Drive engineering and operational excellence best practices across the data infrastructure.
• Mentor and develop peers in the data engineering field.
• Make trade-offs between short-term delivery needs and long-term architectural scalability.

• Employees at Amazon are often offered comprehensive health benefits—including multiple medical plan options (no pre-existing condition exclusions, 100% covered in-network preventive care), dental and vision plans, a 24/7 medical advice line from day one, expert second-opinion services, and broad mental-health support with several free counseling sessions (including pediatric). Financial wellness typically includes a 401(k) with company match (up to 2%), Restricted Stock Units (equity), FSAs, an emergency savings program, product and partner discounts, and even college-savings and home-purchase programs. Overall, the package is designed to support employees and their families’ health, finances, and day-to-day life.