Manager, Data Engineering, Selling Partner Insights and Analytics

Join Amazon’s Selling Partner Insights and Analytics (SPIA) organization as a Data Engineering Manager, where you'll lead the data infrastructure powering 200+ business teams across Amazon Stores. Shape the future of data engineering for a platform processing millions of cases daily, driving insights that directly impact customer experience and operational excellence across the world's largest e-commerce ecosystem.

Amazon's Paragon platform stands as the backbone of case management operations across Amazon Stores, serving as the central nervous system for seller support, compliance investigations, and advertising operations. As our platform continues its evolution from legacy Oracle systems to modern
cloud-native architecture built on AWS services, we're seeking an exceptional Data Engineering Manager III to lead our data infrastructure team through this transformative journey.

Paragon operates at Amazon scale, managing a multi-tenant architecture supporting over 200 distinct business groups, each with unique data requirements, compliance needs, and operational patterns. Our data platform processes terabytes of case data daily, flowing through sophisticated ETL pipelines powered by Datanet, transforming raw operational data into actionable insights that drive business decisions across Amazon's global operations.

Your team will own the critical data infrastructure that enables real-time analytics, historical trend analysis, and predictive insights for business teams
spanning continents and time zones. You'll architect solutions that balance the competing demands of data freshness, query performance, cost optimization, and regulatory compliance. The data engineering challenges are substantial: managing schema evolution across 200+ tenants, ensuring data quality and consistency, implementing robust security controls with KMS encryption, and building scalable pipelines that can handle exponential growth while maintaining sub-second query latencies for critical operational dashboards.

As Data Engineering Manager, you'll lead a team of talented data engineers responsible for the entire data lifecycle—from ingestion and transformation to storage, access, and archival. You'll drive the technical strategy for our migration from Andes to AWS Lake Formation, ensuring zero disruption to downstream consumers while unlocking new capabilities for data sharing and cross-team collaboration. Your leadership will be crucial in establishing data governance frameworks, implementing data quality monitoring, and building self-service analytics capabilities that empower business teams to derive insights
independently.

This role demands both technical depth and leadership excellence. You'll partner closely with product managers, software engineers, and business stakeholders to translate complex data requirements into elegant engineering solutions. You'll mentor and grow your team, fostering a culture of operational excellence, innovation, and customer obsession. You'll own the operational health of data pipelines processing billions of records, ensuring 99.99% reliability while continuously improving efficiency and reducing costs.

The ideal candidate brings deep expertise in distributed data systems, modern data warehouse architectures, and cloud-native technologies. You understand the nuances of multi-tenant data isolation, have battle scars from scaling data platforms through order-of-magnitude growth, and possess the strategic vision to anticipate future needs while delivering immediate business value. If you're passionate about building data infrastructure at scale and want to impact millions of customer interactions daily, this is your opportunity to leave a lasting mark on Amazon's operational excellence.

About the team
Selling Partner Insights and Analytics team mission is to build listening mechanisms to gather frictionless SP feedback, generate insights at scale, and enable teams to prioritize and measure improvement opportunities. Selling Partner Insights and Analytics focuses on surfacing SP sentiment data/insights and connecting our stakeholders to actual SP experiences. The team builds data and reporting infrastructure for 20k+ stakeholders worldwide.

Basic Qualifications

- Bachelor's degree
- 5+ years of data engineering experience
- 3+ years of processing data with a massively parallel technology (such as Redshift, Teradata, Netezza, Spark or Hadoop based big data solution) experience
- 3+ years of relational database technology (such as Redshift, Oracle, MySQL or MS SQL) experience
- Experience hiring, developing and promoting engineering talent
- Experience leading and influencing the data or BI strategy of your team or organization
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- 3+ years of data engineering experience

Preferred Qualifications

- Master's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
- Experience with AWS Tools and Technologies (Redshift, S3, EC2)

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