Data Engineer for Crowdsourced Sensing

The Sensing and Connectivity team is committed to developing innovative technology that enhances user safety and makes our devices more intelligent. We are exploring how the incredible sensing, processing, and connectivity of iPhone can be leveraged to detect nearby hazards and deliver timely alerts. We are seeking a data engineer with strong experience in distributed systems, real-time data pipelines, and large-scale telemetry processing to help build the data infrastructure that enables algorithms to operate reliably at a global scale.
In this role, you will design, build, and maintain scalable data pipelines that ingest, process, and transform large volumes of crowdsourced sensor data from heterogeneous mobile and edge devices. You will develop real-time streaming systems, create simulation and replay frameworks, support algorithm experimentation at scale, and ensure data quality across highly dynamic and noisy environments. You will work closely with machine learning engineers, hardware teams, and software engineering partners to architect data flows, optimize end-to-end latency, and deliver robust, production-ready systems that support features deployed on millions of devices worldwide.
<h3>Minimum Qualifications</h3>5 years of experience developing data ingestion, transformation, and validation pipelines for noisy or heterogeneous datasets
5 years of experience with cloud or distributed storage systems (S3, HDFS, object stores) and columnar formats (Parquet, ORC)
3 years of experience designing real-time or distributed data pipelines with stream-processing frameworks (Kafka or equivalent)
3 years of experience managing large-scale telemetry or sensor-data systems
<h3>Preferred Qualifications</h3>Graduate degree in Computer Science, Data Science, or equivalent experience.
Experience with IoT or mobile-sensing data, including intermittent connectivity and edge-generated telemetry
Experience with time-series data systems, sensor pipelines, or environmental/earth science data
Experience supporting simulation, replay, or regression pipelines for algorithm evaluation
Experience with data-quality frameworks, schema management, and observability tools