Amazon

Quality Assurance Engineer, Trust Sensitive C&Intelligence

Job Description

Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa.

In this role, QAE will exhibit technical capability to execute automated test scripts, develop utilities or tools to improve efficiency and gather / analyse logs and traces to add to bug reports. QAE will try to possess detailed functional understanding to execute assigned test cases. QAE can interpret test results and distill information into easily digestible format to QS associates. QAE will collaborate with QA teams to improve test coverage through test case optimizations and ad-hoc defects to test cases conversions. QAE can reproduce errant product behavior to assist developers or other testers and identify, track, and accurately report defects found.

Key job responsibilities
- You execute a set of test cases assigned to you by an SDET/QAE/ your manager. You write and maintain black box test cases with some guidance.
- You can drill down on ambiguous tasks requests (new features / use cases) into clear work instructions through first pass execution or training from QA, which in turn helps team to start testing without any hiccups.
- You can install and configure software, can use debug settings. You have technical ability to gather logs, traces,… to add to bug reports when provided detailed instructions.
- You can give feedback on automation needs to your team / QA team for the components owned, execute and verify results of existing automated tests, manage and scale the lab infrastructure based on project needs.
- You have basic understanding of the automation framework to initiate, manage and maintain automation runs as per defined schedule/SLA.
- You reproduce errant product behavior to assist developers or other testers and identify, track, and accurately report defects found. Good reproducible defect reports include; steps to repro, logs, clear description of actual results vs. expected, helpful title / summary. You verify fixed bugs are truly fixed.
- You perform defect analysis and helps prioritize and escalate issues in areas within your scope. Gives feedback on coverage, quality of test instructions and acts as a catalyst to process sustenance.
- You analyze automation failures and report issues by isolating script/product failures
- You come up with initiatives which includes develop simple tools / utilities that helps the project key metrics like productivity, defect leakage and quality in test areas owned. You help in improving the stakeholder communication processes.

About the team
Trust Sensitive Content Intelligence team leverages natural language understanding, machine learning, and big data to help Alexa identify sensitive content. We are looking for a talented Software Development Engineer who can ensure that customers continue to have fun engaging conversation with Alexa.

We are a highly collaborative, fast moving, and fun team. Our ideal candidate is highly autonomous, possesses strong written and verbal communication skills, strong sense of ownership, adapts quickly to new technologies, and has high career growth potential. The desire and ability to work in a fast paced, collaborative environment is essential. Candidate should have a solid understanding of software design principles, a demonstrated track record of delivering new features and passion for innovating on behalf of customers.

Candidates don’t need to have expertise in natural language understanding or machine learning but should have curiosity about it. They value collaboration, data-driven decision making, learning, and can comfortably communicate with others, in both technical and non-technical roles.- 4+ years of quality assurance engineering experience
- Experience in manual testing
- Experience in automation testing
- Experience scripting or coding
- Experience as QA lead on medium to large sized projects


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