Camera Hardware Data Engineer
Referral available at Apple
An employee here can refer you. The referrer stays anonymous and reaches out to you directly if interested.
Summary
Builds scalable data pipelines and solutions for Apple's camera hardware teams, bridging hardware engineering, data science, and manufacturing operations. Works with cloud platforms like Snowflake/Databricks, data transformation frameworks (dbt/Spark), and Python-based tools to support camera R&D and production workflows.
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 team's 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.
Minimum Qualifications
- 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 techniques
Preferred Qualifications
- 10 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