Data & Analytics Engineer, Health & Fitness, Sensing & Connectivity

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. The Applied Sensing & Health team builds the pipelines and infrastructure that turn raw motion data from Apple devices into insights that help users improve their health. Every time someone exercises or moves with their device, engineers and scientists on this team are behind the systems that capture, validate, and make sense of that data at scale. As a member of our dynamic group, you'll build and monitor the data pipelines that power our products, run large-scale analyses that uncover how people actually use and benefit from them, and help shape upcoming products that delight and inspire millions of Apple customers every day. Join our team and enrich lives by shipping software and data systems that motivate fitness goals, improve health, and enhance the way customers interact with our products. Description In this role, you will design and build the data infrastructure that powers our next generation of Health and Fitness features — from ingesting raw sensor data to the platforms and tools that turn it into research insight. This includes architecting scalable data pipelines, ensuring user study data quality, gathering algorithm performance insights, and validating the software implementation. Key responsibilities of this role are: Design and develop scalable, parallelized data pipelines to process and transform complex, time-series health data captured from consumer-grade sensors Build tools and dashboards to visualize data and support data exploration, analysis, and research insights. Collaborate with study stakeholders to gather requirements and contribute to the study design, tooling development, and data quality monitoring processes. Design and run analytics to validate algorithm simulation output against ground truth, and build automated QA metrics to track feature performance over time Work closely with cross-functional teams including Software, Algorithms, and EPM to support design, integration, testing, and deployment of the health software. Minimum Qualifications BS degree in Computer Science, Engineering, Data Science, or a related technical field. Minimum of 3 years of industry experience building and maintaining large-scale data processing or simulation pipelines, or performing large-scale data analysis. Strong programming skills in Python, with a focus on writing clean, maintainable, and high-performance code. Familiarity with SQL for querying, transforming, and analyzing structured data. Hands-on experience with distributed computing frameworks such as Apache Spark. Comfort leveraging AI tools to accelerate problem-solving and development. Preferred Qualifications Applied machine learning experience, including building models on large datasets Experience working with sensor data, time-series data, or healthcare datasets Experience in cloud environments (AWS, GCP, or Azure) with containerization tools like Docker and Kubernetes Comfort working in Linux environments, including scripting, navigation, and basic troubleshooting Exposure to clinical studies, FDA validation processes, or regulated health environments Experience building iOS apps; familiarity with C++ is a plus Self-directed and self-motivated, with experience producing architecture and design documents Strong communication and cross-functional collaboration skills, including translating requirements from technical and non-technical partners into practical engineering tasks #J-18808-Ljbffr Socket.dev