Job Abstract

The Apple Watch EE team has a dynamic opportunity for a test / data engineer who is passionate about using data to discover hardware interaction issues during the design processes, and influence the design. This is a deep collaboration with System EE and hardware module teams for test design and factory data collection.If you have a deep interest in data, designing and developing scaleable automation infrastructure, applying data engineering techniques on terabytes of data, architecting storage and retrieval, processing data, analyzing failures for root cause, and creating immersive data visualization, come join our team to make a lasting impact!

Deep interest in how data engineering helps product integration and system design challenges, experience using Python frameworks and libraries for scientific computing (e.g. Numpy, Pandas, Pytorch)Strong proficiency in Python with modern software principles, and OOPAdept at version control systems and CI/CD best practicesNavigates ambiguity in work in a fast-moving environment with multiple stakeholders, distilling multi-dimensional problems to important constituent componentsSkilled at distilling questions and needs down to requirements for implementationPractical computer architecture and OS/SW debugging skills (e.g. firmware debug, triage and isolate software bugs)

- Collaborate cross-functionally for test design, and implement at-scale factory data collection, with a strong emphasis on timely data delivery, experiment prioritization, and understanding underlying issues and questions- Debug of various coex and hardware issues with System EE and Module EE teams, building custom build scripts, and data insights to pinpoint relevant factors- Deep-dive into data and outliers, applying relevant mathematical, statistical, and visualization techniques-Ensure automation, tooling, infrastructure and data pipelines are continuously and efficiently delivering data, implementing ideas and optimizations for both code and processes

The following skillsets are considered a plus:- Practical knowledge of cloud infrastructure and implementation of scalable automation solutions (e.g. Kubernetes, AWS, Spark, Celery, Postgres, RabbitMQ)- Practical knowledge of system integration issues in hardware design- Exposure to sampled systems (e.g. sensors, DSP), and various radio technologies (e.g. LTE, WiFi, BT, NFC)