Platform & Data Engineer: Scale Kubernetes & MongoDB

Apple Inc.; Cupertino (CA); On-site; USD 150,400 - 277,600; Full time

Job summary

Apple Inc. seeks a Platform & Data Engineer to own the systems that power thousands of internal engineers. You will operate at the intersection of Kubernetes platform engineering and large-scale data engineering, owning both compute platforms and data layers.

You will build internal tooling APIs, improve observability, and enable teams to self-serve data questions. You will manage MongoDB ingestion at scale, optimize queries, and design ETL pipelines while collaborating across teams to balance

Qualifications

• Bachelor's degree or equivalent practical experience in computer science or related field.
• 3–5 years of professional software engineering with hands-on production systems experience.
• Deep Kubernetes experience at scale with performance improvements.
• Experience with MongoDB and aggregation patterns for large-scale data.
• Fluency in Python and comfortable with code in production.

Responsibilities

• Own the Kubernetes platform engineering and data engineering stack used by Apple internal tools.
• Lead observability, error tracking, and log aggregation across services at scale.
• Design and build internal tooling APIs with multi-tenancy, authentication, and capacity planning.
• Develop self-service surface for platform adoption and SDK integration for teams.
• Lead MongoDB estate ingestion, query optimization, indexing, and sharding as data grows.
• Own ETL pipelines feeding the data layer and enable self-service data querying.

Skills

Python

Kubernetes

MongoDB

Data engineering

Observability

APIs design

ETL pipelines

LLM-assisted tooling

Platform engineering

Production systems

Education

Bachelor's degree in CS or related

Tools

SDK development

Job description

Apple Inc. seeks a Platform & Data Engineer to own the systems that power thousands of internal engineers. You will operate at the intersection of Kubernetes platform engineering and large-scale data engineering, owning both compute platforms and data layers.

You will build internal tooling APIs, improve observability, and enable teams to self-serve data questions. You will manage MongoDB ingestion at scale, optimize queries, and design ETL pipelines while collaborating across teams to balance