SSE - Backend

Amazon • Doctoral Degree (PhD) • Python • ETL/ELT • Dimensional Modeling • 22 days ago
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Salary
≈ $18k – $44k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Middle · 3+ years exp

First seen by Alion on Sep 28, 2026. Amazon scores B on the Alion truth index.

Overview
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Amazon

Amazon is an American technology and retail conglomerate founded by Jeff Bezos in 1994 as an online bookstore and headquartered in Seattle, Washington. It operates the world's largest online marketplace together with a global logistics network, physical grocery stores and a third-party seller platform that accounts for most units sold. Amazon Web Services, launched in 2006, is the leading public cloud provider and generates the majority of the group's operating profit, while advertising, Prime Video, Alexa devices and Kuiper satellite broadband round out the business.
amazon.com • HQ: Seattle, United States • Robotics AI • Logistics & Warehouse Robotics • Coupons, Deals & Cashbacks • Gift Cards & Loyalty Programs • Commerce • Advertising • Logistics • Streaming & OTT Platforms • Cloud Platforms (IaaS & PaaS) • Marketplaces • 5000+ employees • Est. 1994
Tech stack of Amazon
Backed by General Catalyst, Kleiner Perkins, Plug and Play

Requirements:

  • Education: B. E. /B. Tech. /M. E. /M. Tech. /PhD from Tier-1 engineering institutes.
  • Experience: 3+ years in backend and infrastructure, preferably from strong product/technology companies.
  • Core engineering: Strong in system design, architecture, scalable backend development, and complex codebases.
  • Distributed systems: Experience with large-scale databases, high-throughput messaging systems, real-time job queues, and distributed/large-scale systems.
  • Data Engineering: Strong experience building high-scale data platforms.
  • Data warehousing: Must understand star schema, fact tables, dimensions, and warehouse schema design.
  • Data pipelines: Experience with data movement, ETL/ELT, transformation, integration, and data propagation.
  • Architecture: Ability to design scalable data architectures and make technology/architecture decisions.
  • Preferred stack: GCP, Django, PostgreSQL, Celery, Redis.
  • Bonus: Exposure to AI Infrastructure / AI Operations.
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