Camera Hardware Data Engineer

Apple delivers the most popular cameras in the world. Each product release provides breakthroughs in photography with stunning camera features that customers love. Our cameras deploy imaging complexity at the frontier of traditional camera engineering methods. Data volumes are growing to meet this need across camera simulations, performance calibrations, measurement results, and their correlations. Our teams task is to build a comprehensive aggregate data layer that enables efficient and flexible executive reporting, highly customized data applications, powerful ML inference, and agentic GenAI workflows. In this role, you will work closely with data scientists, hardware engineers, hardware test, and manufacturing operations teams to build scalable data pipelines and solutions. As a camera hardware data engineer, you must effectively collaborate to bridge the gap between business needs, analytical solutions, and engineering requirements. Additionally, proactive collaboration with other data engineering teams is essential for scaling solutions across teams.As part of the Camera Hardware Data Engineering team, you will be responsible for expanding our powerful data engineering platform and custom team tools by designing, developing, and maintaining robust data pipelines to support camera manufacturing analytics initiatives. You will work independently to design technical solutions to process massive datasets and engage with internal and external data providers and consumers to create low-friction data exchange systems. You will provide technical leadership for 3rd party development teams and mentor and provide data engineering best practices across the organization.BS in Computer Science, Data Engineering, Data Science, Math, or related fields Hands-on Experience using cloud data analytics platforms (i.e. Snowflake, Databricks, or similar) Experience building data transformation pipelines using frameworks such as Data Built Tool (dbt) or Spark Experience in data modeling and data governance techniques10 years of relevant industry experience Experience in building and validating AI tooling such as MCP servers, automated agents, and RAG pipelines Experience with pipeline orchestration frameworks such as Airflow Experience in the use of Python frameworks like FastAPI to build cloud-native data access tools Experience designing and building relational databases (i.e. PostgreSQL) and non-relational databases (i.e. Redis, MongoDB) Working knowledge of Kubernetes for deploying and monitoring cloud-native applications