Apple needs to build a comprehensive aggregate data layer to enable efficient and flexible executive reporting, highly customized data applications, and powerful ML inference and analysis for their camera products.
Requirements
- Hands-on Experience using cloud data analytics platforms (i.e. Snowflake, Redshift, BigQuery)
- Experience building data transformation pipelines using frameworks such as Data Built Tool (dbt) or Spark
- Experience in data modeling and data governance techniques (i.e. Row Access Policies, role-based access control (RBAC)
- Experience with pipeline orchestration frameworks such as Airflow
- Experience using BI tools such as Tableau or app frameworks such as Streamlit or Superset to build shareable and easy-to-understand data visualizations
- Experience in the use of Python frameworks like FastAPI to build cloud-native data access tools
Responsibilities
- expanding our powerful data engineering platform and custom team tools by designing, developing, and maintaining robust data pipelines to support camera manufacturing analytics initiatives.
- design technical solutions to process massive datasets
- engage with internal and external data providers and consumers to create low-friction data exchange systems.
- provide technical leadership for 3rd party development teams
- mentor and provide data engineering best practices across the organization.
Other
- work closely with data scientists, hardware engineers, hardware test, and manufacturing operations teams
- effectively collaborate to bridge the gap between business needs, analytical solutions, and engineering requirements.
- proactive collaboration with other data engineering teams is essential for scaling solutions across teams.
- BS or higher in Computer Science, Data Engineering, Data Science, Math, or related fields
