Senior Data Engineer, Strategic Partnerships & IMPACT360

Amazon • Seattle, WA

2d • $154,600 - $209,100 • Onsite

About The Position

Amazon Web Services (AWS) is seeking a Senior Data Engineer to join a centralized business development team that manages strategic partnerships across all of Amazon. This team generates, manages, and executes complex and high-impact partnership deals, managing relationships and negotiations for partnerships that have broader implications to AWS and other Amazon business units. This role will be part of the Strategic Initiatives team, focusing on technical analysis, adaptability, and action-oriented decision-making. The team values creative, proactive individuals who seek opportunities to advance Amazon's business growth initiatives.

This is a senior data engineering role on a small, technical team within the AWS Specialist and Partner Organization (ASP). The role involves owning the data architecture for key domains of an internal deal intelligence platform. This platform unifies Amazon's buying and selling activities into a single decision framework for leaders, integrating AWS revenue, vendor spend, contract structures, and competitive dynamics. It ingests data from thousands of buy-side agreements and dozens of upstream systems, resolves real-world entities into trusted relationships, and powers analytics, forecasting, and AI layers. The engineer will own design within their domains and shape architecture decisions for the BI and ML layers.

The engineer will operate in areas where business problems are defined but technical approaches are not yet established. A central part of the work involves evolving how the platform sources, models, and serves data, moving towards governed, reusable, directly consumed data products with incremental, retry-safe, and atomically published datasets. The engineer will help shape the target architecture and drive migration within their domains without disrupting critical daily pipelines for finance and leadership.

Responsibilities include onboarding and integrating data from various Amazon teams (AWS Sales, Procurement, Finance, Retail, vendor systems), investigating source-system behavior, resolving conflicts from inconsistent data, and driving alignment across organizations that have not previously shared data. This involves cross-team investigation and stakeholder management as much as coding.

The engineer will design and operate scalable data systems within their domain to serve multiple stakeholders with different access patterns: batch analytics for finance, governed and row-level-secured reporting for leadership, and curated datasets for model training. This will involve working across the full data-engineering stack on AWS, including distributed data processing, workflow orchestration, an open table-format lakehouse, a SQL query and serving layer, governed cross-account data sharing, and BI. The role also includes partnering with the science team to build data infrastructure for forecasting and reinforcement-learning initiatives.

Amazon is an equal opportunity employer committed to diversity and inclusion. They offer comprehensive benefits including health insurance, 401(k) matching, paid time off, and parental leave. The base salary range for this position in Seattle, WA is $154,600.00 - $209,100.00 USD annually, with additional sign-on payments and RSUs.

Requirements

• 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)

Nice To Haves

• Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda
• Experience designing data architecture and end-to-end data flows in ambiguous, loosely-defined problem spaces
• Experience with open table formats (Iceberg, Hudi, or Delta Lake) and lakehouse architectures on a cloud object store
• Hands-on experience with the AWS data stack (Glue, Athena, Lake Formation, S3, IAM) and governed cross-account data sharing
• Experience with entity resolution, record linkage, or building unified/"golden" 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 many disparate source systems, and driving cross-team alignment on data definitions and ownership
• Experience modernizing or re-architecting legacy data pipelines toward reusable, governed data products
• Track record of mentoring engineers and driving operational excellence (data quality, reliability, observability)

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

Benefits

• sign-on payments
• restricted stock units (RSUs)
• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed