Data Engineering Quality Manger
Santa Clara Valley (Cupertino), California, United States
If you’re passionate about using data to shape exciting new technologies, come join us in the Human Input Devices team. Quality is a critical part of everything we do, as a Quality Manager in HID you will lead a team of passionate engineers, working cross-functionally in a fast-paced environment, facing exciting new challenges daily. You possess a high-energy personality with excellent communication and interpersonal skills, attention to detail, patience, and a tenacity for high quality data. Come join us in crafting solutions the world hasn’t seen yet.
- Passionate about user experience.
- Excellent communication and influencing skills.
- Comfortable working in a constantly changing, dynamic environment.
- Consistently drive issues to resolution.
- Proven track record of demonstrating the ability to attract, hire, motivate and develop top talent.
- 2+ years experience as a QA Manager, experience managing managers a plus.
- Have worked in live production data environments with large scale data pipelines
- Ability to understand low level software requirements and build comprehensive test plans
- Experience with design and implementation of large scale test automation
- Linux/Unix background, familiarity with networking tools
- Familiarity with iOS/MacOS frameworks a plus
We are looking for an experienced QA manager to focus on quality for large scale user study applications and data pipelines. The Quality Manager will interact with all engineering disciplines including software, algorithm, user study operations and project management teams. You will oversee the data lifecycle from user interactions to high quality, useable data, ready for consumption by algorithm research teams. This will require innovative solutions to ensure robustness and high data availability. You will thrive in a high pressure environment, with lots of moving parts and interfaces.
Education & Experience
Bachelor's degree or equivalent industry experience in a related discipline (Computer Science, Data Science, or Software Engineering).