QAE II, Amazon Business Operations, Amazon Business Operations

Amazon Business customers place orders that arrive by the pallet: a school district ordering 2000 chairs a contractor ordering 900 boxes of fasteners a hospital restocking a floor. Amazon Business Operations builds the systems that get those orders sourced consolidated and delivered on the date the customer asked for. When one of these orders goes wrong it does not just degrade a page: a dock appointment is missed an operator recovers the order by hand and an enterprise reconsiders how much of its spend comes to Amazon.
We are building a new engineering team in Hyderabad dedicated to bulk fulfilment and this is its first quality engineering hire. There is no existing test practice to inherit. You will decide what good looks like for every service the team launches.

Key job responsibilities
As a Quality Assurance Engineer on this team you will:
Own feature quality end to end: design test plans execute test cases run bug triage coordinate with product and engineering stakeholders and give release sign-off.
Build and maintain automated test coverage inside the teams framework so that regression testing does not grow with the codebase.
Test operator-facing tooling and machine learning assisted decision flows where correctness means the recommendation a human acts on is right not only that the service returned a response.
Validate integrations with Amazon supply chain and fulfilment systems including the data correctness that inventory transportation and delivery promises depend on.
Set the quality bar for a new team: definition of done quality metrics and production monitoring that catches problems before an operator does.
You will work directly with the engineers building the service the product manager writing the requirements and the Amazon Business Operations teams who use what you ship. If you want ownership of quality for a product line from its first commit this is that role.

A day in the life
You will sit with the engineers building the service not apart from them. A typical day mixes writing and running test cases for the feature in the current sprint extending automated regression coverage in the teams framework and triaging yesterdays defects with the SDE who owns the code. You will review requirements with the product manager before a story enters the sprint so acceptance criteria are testable rather than aspirational. You will spend time with the Amazon Business Operations teams who use the tooling because they will tell you which failures actually hurt. Before a release ships your sign-off is the gate.

About the team
Amazon Business Operations builds the systems and processes behind business buying at Amazon from quoting a large order to landing it on a customers dock. This is a new engineering team inside it dedicated to bulk fulfilment and you are joining at the start. We work in small teams with direct ownership we measure ourselves on what changed for the customer and we expect engineers to argue about design in writing. Expect high autonomy close contact with the operators and customers you build for and problems that have not been solved anywhere in Amazon yet.

- 4 years of quality assurance engineering experience
- Experience in automation testing
- Experience in manual testing
- Experience as QA lead on medium to large sized projects
- Experience developing high quality test plans test designs test strategies and test execution
- Bachelors degree in computer science or equivalent
- Experience scripting or coding
- Experience troubleshooting and debugging technical systems

- Experience in gathering test requirements to create detailed test plans and defining quality metrics to measure product quality
- Experience programming with at least one modern language such as Java C or C# including object-oriented design
- Experience in penetration testing and exploitability-focused vulnerability assessment
- Knowledge of overall system architecture scalability reliability and performance in a database environment
- Experience debugging diagnosing and troubleshooting complex production software issues

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