Full-Stack Software Engineer - Device Telemetry

Apple Inc

Quick summary

Work type: On-site
Location: San Diego, CA
Salary: $139,500–$258,100 / yr
Posted: 61 days ago
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $177k

This role $199k

$125k most similar roles pay here $272k

This role pays more than 57% of similar roles. Most pay $142,400–$211,200 — the shaded band above. At the midpoint, this role pays about $199k versus about $177k for comparable roles.

Based on 240 similar postings.

Employer

About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 1723 open roles on FindRole.

Listed pay typically runs $162,500–$272,100 across 1398 roles with salary data.

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At a glance

TL;DR · Full-Stack Software Engineer - Device Telemetry

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Apple's Data Analytics team seeks a Full-Stack Software Engineer to join their small, tight-knit group focused on developing services and applications for parsing and understanding data from Apple devices worldwide. This role involves designing, building, and iterating on stream processing and ingestion pipelines, collaborating closely with data scientists and engineers across the organization. Day-to-day responsibilities include implementing web apps and REST APIs, working with databases like Postgres and cache layers such as Redis, and utilizing container-based orchestration tools like Kubernetes. The ideal candidate will have experience with public cloud platforms and a commitment to CI/CD practices, while familiarity with UX frameworks, data visualization, and query engines like Presto or Trino is preferred.

Skills

Kubernetes AWS Postgres Redis CI/CD React Angular Typescript Presto Trino C++

What you'll do

Design and build web backend services for parsing and analyzing data from Apple devices.
Develop REST APIs to facilitate communication between different components of the analytics platform.
Implement stream processing and ingestion pipelines using modern cloud orchestration tools.
Collaborate with data scientists to capture requirements, validate design decisions, and implement solutions.
Enhance database performance by optimizing queries and utilizing cache layers like Redis.
Ensure high availability and scalability of services through container-based orchestration technologies.