Software Quality Assurance Engineer

at  Fluency Inc

TEAM OVERVIEW:

At Fluency, we come together under two shared passions that drive our culture: the scaling of digital ad operations, and fostering a workplace environment rooted in open communication, inclusivity, and a growth-oriented mindset. These passions not only set us apart in the industry, but also fuel our collective drive to reshape the industry and reinvent ourselves along the way. We’re not just revolutionizing the digital advertising horizon, we’re shaping an organization where every member feels a sense of pride and is inspired to do their best work.
The Fluency platform is built using a native AWS architecture. We use Java, Spring Boot, JavaScript, Vue.js, Aurora, Kinesis, S3, Elastic Beanstalk along with whatever else makes sense for the job at hand.

JOB SUMMARY:

The Software Quality Assurance (SQA) Engineer has two areas of focus. First, this position requires a strong mind for software development and works closely with product and engineering partners to build best-in-class quality software. You must be able to manage ideas, timelines and deliverables in a startup environment with aggressive deadlines. Second, this position works on the release side to help code automated tests that prevent bugs from being released live and minimize inbound issues. This position helps the company manage risk through writing functional code tests that help find code problems early. You will collaborate with the Engineering team and distribute the task load to the optimal engineer for fixes that are appropriate to hand off. The ideal candidate must be a self-starter with great communication skills and who can drive ideas and execution while helping further software reliability and QA discipline.

• Build automated functional and regression testing.
• Lead and Support efforts to automate testing.
• Create and maintain test plans for new and existing features.
• Perform stress and load testing of applications.
• Keep the team updated on the status of release and communicate obstacles and testing risks.
• Use modern infrastructure tools and platforms to automate our systems.
• Solve challenging problems working with large dataset transformations.