My First Weeks at Amazon — HYD13

I joined Amazon on 22 September 2025 as SDE 1 at HYD13 — Amazon’s largest campus in India (Hyderabad). It’s been two weeks since I joined, and the experience so far has been intense, humbling, and hugely educational.

First impressions: scale, ownership, and velocity

On my very first day, the thing that hit me most was scale. HYD13 is massive, but beyond the buildings it is the scale of the org, services, and engineering that stands out. My team — Worldwide Recommerce and Tech — owns systems and processes that touch millions of customers and transactions. The footprint of my organisation alone is larger than many top banks and MNCs.

People here don’t just write code; they build end to end services. Amazon has practically every development tool you can imagine — and most of them are internally built. There’s a proprietary tool for deployments, observability, patching, CI/CD, feature flags, experimentation, data pipelines, code review workflows, and more. The ecosystem is so broad that it can take months just to be comfortable with the toolset.

My role and team

I joined the “Worldwide Recommerce and Tech” team — a critical operation that enables recommerce flows across markets. Recommerce involves a lot of moving parts: inventory, pricing, return processing, marketplace integrations, fraud detection, and customer experience. Our team collaborates across product, operations, data science, and infra to ensure these systems run reliably.

From day one, the level of responsibility expected of engineers was palpable. Engineers at Amazon are builders and operators: we design, implement, test, and run the services we write. Ownership is end-to-end. It’s not uncommon to ship a change and then monitor its behavior in production, iterate on metrics, and take ownership of rollout issues.

Customer obsession and long-term thinking

One of Amazon’s core principles — customer obsession — is not a slogan here; it’s a practice. Every design discussion begins with the customer and how a change impacts their experience. That orientation changes technical decisions: we prefer resilient, maintainable designs that protect customers even if they’re more work upfront.

Long-term thinking is baked into the engineering process. Teams plan for durability and scale. Design docs are written with operational considerations, cost implications, and future growth in mind. This approach encourages simplicity and accountability.

Technical learnings so far

Internal tooling: I’ve learned the basics of Amazon’s internal deployment and CI tooling. The tooling emphasizes safety — automated rollbacks, staged rollouts, and tight metrics driven release gates.
Observability: production telemetry (logs, metrics, traces) is first class. Dashboards and automatic alarms help us react quickly.
Testing & quality: testing spans unit, integration, and simulated downstream failures. Chaos style tests and canaries are commonly used for risk mitigation.
Data driven decisions: experiments and feature flags power incremental launches and validate hypotheses with real user data.

These practices are not unique to Amazon, but their maturity and integration into the development lifecycle are exceptional.

How big teams make huge impact

Amazon is composed of many organisations that are essentially companies in themselves. Teams like Prime Video, AWS, Amazon Pay, Fulfillment Technologies, and Devices operate at billion-dollar scale. Within that landscape, recommerce is a crucial piece — not only for the revenue it drives, but for the customer lifecycle and sustainability impact it enables.

When you compare the scale of a single Amazon org to an external large enterprise, the engineering footprint — the number of services, the velocity of releases, and the operational maturity — is often much larger.

A quick technical sketch: building a resilient recommerce service

Here’s a high-level view of what a small recommerce microservice might look like inside Amazon:

• API gateway: handles authentication, throttling, and versioning.
• Service layer: stateless microservice(s) exposing business APIs.
• Data store: durable storage with strong consistency where needed (DynamoDB-like) and analytics stores for aggregated data.
• Event bus / streaming: for async workflows (inventory updates, price changes, fulfillment events).
• Worker fleet: for heavy background processing (image processing, fraud scoring, reconciliation).
• Observability: distributed tracing, per-endpoint latency metrics, error budgets.
• Deployment: staged canaries, automated rollbacks, and region-aware replication.

Each of these building blocks is supported by internal services and frameworks that make it possible to assemble robust systems quickly — once you know where to look.

Personal reflections and what’s next

Two weeks in, I already feel like I’ve accelerated a year of learning. The environment forces you to internalise best practices quickly — from writing clear design docs to instrumenting code thoroughly. The bar for ownership and quality is high, and that is the reason teams move fast without sacrificing customer trust.

Next steps for me are simple and practical:

1. Deepen familiarity with internal tooling (CI/CD, deployment workflows, and monitoring).
2. Pair with senior engineers to learn system level design and operational playbooks.
3. Take on a small ownership area and drive an end-to-end feature from design to production.

If you’re curious about life at Amazon or want to discuss technical design patterns for large scale services, ping me I’m happy to share what I’m learning.

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I hope this gives a useful snapshot of my first weeks at Amazon. Thanks for reading!