Data Engineer, Selling Partner Insights and Analytics, SPS

- 1+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Bachelor's degree or above in Computer Science, Computer Engineering, Information Management, Information Systems, or other related discipline
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
- Knowledge of AWS Infrastructure

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Amazon's Selling Partner Insights and Analytics (SPIA) team is looking for an experienced Data Engineer to help architect and build the data platform that powers Paragon, Amazon's second-largest Human-in-the-Loop platform. Paragon processes over 500 million cases every year and serves 200+ teams and 70,000+ users who handle support and investigation work across Selling Partner Services.

This is a high-impact role for a self-starter who thrives in a fast-paced, ever-changing environment and has a genuine passion for building data-intensive applications. With access to vast datasets and a strong organizational focus on AI/LLM, you will help redefine how data is accessed, trusted, and applied across Selling Partner Services, directly influencing customer experience and operational excellence in the world's largest e-commerce ecosystem.

Key job responsibilities
- Design and operate scalable, cost-effective data infrastructure and pipelines on native AWS technologies, curating data for reporting, analytics, and LLM/ML models.
- Define logical data models and architectures that scale with Paragon's growth into Emerging Marketplaces and worldwide use cases.
- Partner with business owners and technical leaders to gather requirements, shape data architecture, and deliver solutions that meet real business needs.
- Drive Best-At-Amazon (BAA) standards for system efficiency, IMR efficiency, data availability, consistency, and compliance.
- Enable efficient data exploration on large datasets and implement data access controls for stand-alone datasets.
- Build automation that raises the bar on operational excellence, and contribute across the full data engineering lifecycle: design, development, testing, and maintenance.