Senior Data Engineer, Strategic Partnerships & IMPACT360

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Amazon AWS is seeking a Senior Data Engineer to own the data architecture for a deal intelligence platform that unifies AWS revenue, spend, contracts and competitive dynamics. The Strategic Initiatives team within the AWS Specialist and Partner Organization collaborates across Sales, Procurement, Finance and vendors to onboard sources, resolve data conflicts, and deliver governed, reusable data products for analytics and ML. You will design and operate end-to-end pipelines using Spark, Airflow, Glue, Iceberg, Athena and Lake Formation, shaping target architectures and cross-account data sharing. 5+ years, strong SQL and Python, data modeling, ETL, and cloud experience are essential. Tips: tailor your resume to demonstrate cross-team data integration, governance, and stakeholder management; quantify impact and highlight mentoring and ML collaboration.

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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 (AWS Sales, Procurement, Finance, Retail, vendor systems, and more), 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.


Key job 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

About the team
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.

Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS?

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture

AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.

Mentorship & Career Growth

We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Basic 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)

Preferred Qualifications

- 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)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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 base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 154,600.00 - 209,100.00 USD annually