Technical QA Engineer (EU)

We're looking for a mid-level Technical QA Engineer to join our team where there's room for everyone to improve their core skills and also get better at mentoring and leadership. The Technical QA Engineer role is part of our QA team; the team responsible for both automated and manual testing as well as monitoring and ensuring the quality and reliability of our services.

What you'll be working on

• Actively participate in the development during the whole lifecycle of the project - starting from requirement clarification through design and even after release
• Perform regular manual and automated testing of our services
• Investigate and troubleshoot issues across the full stack (client, server, data pipelines, infrastructure issues)
• Analyze issues and identify root causes in all parts of our system
• Validate data pipelines and integrations on third-party websites or mobile applications
• Collaborate with engineering teams to resolve defects.
• Develop and maintain tests and test automation

Who you'll be working with

• A small, smart, and highly capable development team that builds and runs their services themselves
• A product team that makes decisions based on usability tests and usage data

The majority of our team is located in Budapest, Hungary, but you'll be able to work remotely anywhere in the EU. #LI-remote

As a member of a small team, you must be experienced in some areas to be productive from day one. Everything else is fair game and you will have the opportunity to learn on the job.

Must-haves

• 3+ years of QA experience with web applications and/or mobile apps
• Proficiency in at least one programming language
• Experience with manual and automated testing frameworks
• Experience working in cross-functional teams
• Familiarity with monitoring and alerting tools
• Experience with CI/CD pipelines and test automation integration

Bonus points

• You have experience defining processes along KPIs
• You have experience with monitoring and alerting tools to prioritize testing and the impact of issues
• You used cloud services before (AWS, GCP, Azure)
• You have worked with larger datasets and patterns in them
• You have worked in a startup environment before
• Comfortable working in a fast-paced environment

If the role sounds interesting, apply now and get to know us during the interviews. You can read more about our hiring process on Glassdoor.

At Secret Sauce, we use the technologies and tools that we believe are right for the job at the time. We're not afraid to replace a technology or rewrite a service if gaining experience and understanding the domain better makes us realize that we made the wrong choice. We embrace change and work in a fast-paced environment which means that the technology stack we work with is what we believe is the best. That makes us quite happy.

Our backend system consists of independent services built using Java and Python that communicate asynchronously through Kafka. We use Avro and a Schema Registry to enforce these interfaces. All our services are packaged using Docker and deployed to our infrastructure in AWS using Kubernetes. Our infrastructure is immutable, we build AMIs with Packer and roll them out with Terraform. We don't have "DevOps" or an Ops team, we think of running services in a cloud environment as part of the software engineering role.

The services we provide to our retail partners are integrated into their existing websites; we provide a single JavaScript library that they can use to unlock all of our products. Analytics, AB testing, error reporting, real-user monitoring is built-in and is available to Fit Predictor, Style Finder, and our future services. The services themselves are built using modern JavaScript, React, and Svelte.

Our data team loves Spark and uses it to process large datasets that we receive from our partners and that we produce ourselves. We don't run a persistent cluster; we process and move data between different data stores: S3, Kafka, PostgreSQL, and Snowflake are all part of the equation and are used where they make the most sense. We rely on Databricks to manage our Spark clusters and use Apache Airflow to orchestrate tasks and to monitor, schedule, and retry jobs.

We started out as a small development team using Ruby and Rails. We ended up with our current architecture and tech stack not because we use technology for technology's sake, but because we believe they are the right choice with the right trade-offs for our expertise, needs, and size.