Amazon is the world’s largest e‑commerce and cloud‑computing company, operating in more than 20 countries and serving millions of customers daily. In India, Amazon has built a robust ecosystem that includes retail, logistics, digital services, and a thriving marketplace for millions of sellers. The company’s culture of customer obsession, innovation, and operational excellence has made it a top employer for technology talent across the globe. The Selling Partner Insights and Analytics (SPIA) team is a critical part of Amazon’s seller‑centric strategy. It powers Paragon, the second‑largest Human‑in‑the‑Loop platform at Amazon, handling over 500 million cases a year for more than 200 internal teams and 70 000+ users. The team focuses on turning massive, heterogeneous data sets into actionable insights that improve seller experience, reduce friction, and enable AI‑driven decision making across Amazon’s marketplace. As a Data Engineer on the SPIA team, you will design, build, and operate scalable data pipelines and infrastructure on native AWS services. You will work closely with product owners, data scientists, and ML engineers to curate data for reporting, analytics, and large‑language‑model (LLM) training. The role demands a blend of strong SQL skills, hands‑on experience with big‑data technologies, and a passion for building reliable, cost‑effective data platforms that can grow with Amazon’s expanding marketplace footprint. Key Responsibilities: 1. Design and maintain cost‑effective, highly available data pipelines on AWS (S3, Glue, EMR, Redshift, etc.). 2. Develop logical and physical data models that support Paragon’s reporting and ML workloads. 3. Build and optimize ETL jobs using Spark, Hive, and Python/Scala scripts. 4. Collaborate with business stakeholders to gather requirements and translate them into scalable data solutions. 5. Implement data governance, access controls, and compliance standards for sensitive datasets. 6. Automate monitoring, alerting, and remediation to achieve Best‑At‑Amazon (BAA) operational metrics. 7. Enable self‑service data exploration for analysts through curated data marts and catalogues. 8. Participate in code reviews, performance tuning, and capacity planning. 9. Contribute to documentation, knowledge sharing, and mentorship of junior engineers. 10. Stay updated with emerging AWS services and industry best practices to continuously improve the data platform. Tech Stack: AWS (S3, Redshift, Glue, EMR, Lambda), Hadoop ecosystem (Hive, Spark), SQL, PL/SQL, SparkSQL, Python, Scala, data modeling tools, ETL frameworks (Informatica/SSIS alternatives), Linux/KornShell scripting. Growth Path: Starting as a Data Engineer, you can progress to Senior Data Engineer, Lead Data Engineer, and eventually Data Architect or Manager – Data Engineering, with opportunities to work on high‑impact projects across Amazon’s global marketplace and AI initiatives. Why Join Amazon? You will work on one of the largest data platforms in the world, influence decisions that affect millions of sellers and buyers, and be part of a culture that rewards invention, ownership, and relentless customer focus. The role offers exposure to cutting‑edge AI/LLM projects, a collaborative environment, and clear career advancement pathways.