Senior Data Engineer, Strategic Partnerships & IMPACT

Amazon Web Services (AWS) is seeking a Senior Data Engineer to join our team. This is a unique opportunity to join a centralized business development team that manages strategic partnerships across all of Amazon. Our 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 our Strategic Initiatives team where we dive deep and provide thoughtful technical analysis, but are adaptable and action‑oriented, focused on quickly gaining enough context to enable informed decision‑making. AWS Strategic Initiatives is a small, tight‑knit team that values authentic, strong‑willed individuals who think creatively and will proactively seek out opportunities to advance the growth initiatives of Amazon's businesses.

This position is part of the AWS Specialist and Partner Organization (ASP). Specialists own the end‑to‑end go‑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.

This is a senior data engineering role on a small, technical team. You will own the data architecture for key domains of an internal deal intelligence platform, the system that unifies what Amazon buys and what it sells into a single decision framework that leaders rely on for portfolio decisions. The platform fuses AWS revenue, vendor spend, contract structures, and competitive dynamics, ingesting data from thousands of buy‑side agreements and dozens of upstream systems, resolving messy real‑world entities into trusted relationships, and powering the analytics, forecasting, and AI layer on top.

You'll own the design within your domains and shape the architecture decisions the BI and ML layers above depend on.

You will operate where the business problem is defined but the technical approach is not. As the platform's role grows, a central part of this work is evolving how it sources, models, and serves data: moving toward governed, reusable, directly‑consumed data products, with incremental, retry‑safe, and atomically published datasets. You'll help shape that target architecture and drive the migration within your domains, without disrupting the pipelines finance and leadership depend on daily.

You will onboard and integrate data from teams across Amazon, investigating source‑system behavior, resolving conflicts across inconsistent real‑world data, and driving alignment across organizations that have not shared data before. This work is as much cross‑team investigation and stakeholder management as it is code.

You will design and operate scalable data systems within your domain that 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. You'll work across the full data‑engineering stack, including distributed data processing, workflow orchestration, an open table‑format lakehouse, a SQL query and serving layer, governed cross‑account data sharing, and BI, on AWS (today, technologies such as Glue/Spark, Airflow, Iceberg, Athena, and Lake Formation, evolving as we modernize).

The specific tools matter less than the judgment to choose the right one, simplify complexity, and build systems that are extensible and easy to operate. You will also partner with our science team to build the data infrastructure behind forecasting and reinforcement‑learning initiatives, including feature pipelines, training datasets, decision logs, and reward signals.

• 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…