Senior Backend Software Engineer, Platform Engineering, Services Data Science & Analytics

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Cupertino, CA
Salary
$184,700–$324,800 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $188k
This role $255k
$130k most similar roles pay here $346k

This role pays more than 94% of similar roles. Most pay $151,000–$225,000 — the shaded band above. At the midpoint, this role pays about $255k versus about $188k 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 3562 open roles on FindRole.

Listed pay typically runs $166,200–$277,600 across 2758 roles with salary data.

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At a glance

TL;DR · Senior Backend Software Engineer, Platform Engineering, Services Data Science & Analytics

As a Senior Backend Software Engineer, Platform Engineering, Services Data Science & Analytics, you will join the Platform Engineering team to design, build, and operate backend services that form the foundation of the organization's data platform. You will own core platform services end-to-end, including API design, query routing, caching infrastructure, and data lineage while ensuring reliability at high scale. The role involves collaborating with Data Science and Machine Learning Engineering teams to shape architectural decisions for scalability and durability. You will utilize Python or Node.js/TypeScript, and work with technologies such as FastAPI, Express, Redis, Snowflake, Trino, Spark, PostgreSQL, and Kubernetes. Your work addresses the critical problem of managing, governing, and serving data at scale for large e-commerce and media streaming businesses while ensuring performance and reliability across distributed systems.

What you'll do

  • Design and build backend services for the core data platform infrastructure.
  • Manage end-to-end service components including API design, query routing, and caching systems.
  • Ensure high performance and reliability for services operating at massive scale.
  • Make architectural decisions to improve the scalability and durability of analytical foundations.
  • Develop and maintain multi-layer caching architectures and data lineage systems.
  • Integrate backend services with distributed data stores like Snowflake, Trino, or Spark.
  • Implement observability practices using tools like Prometheus, Grafana, and OpenTelemetry.
  • Drive the technical roadmap for managing and governing data at scale.

What we're looking for

  • 8+ years of experience building and operating high-performance, production-grade backend API services.
  • Expert proficiency in Python or Node.js/TypeScript including async programming, type safety, and framework-level development.
  • Demonstrated experience designing and operating distributed systems with strict reliability and latency SLAs.
  • Strong proficiency with API design and service-to-service communication patterns like REST, gRPC, or GraphQL.
  • Hands-on experience with multi-layer caching architectures and cache invalidation strategies.
  • Extensive experience building systems that integrate with high-performance data stores such as Snowflake, Trino, Spark, or PostgreSQL.
  • Proficiency with Kubernetes, deployment, HPA, StatefulSets, and namespace management.
  • Strong observability practice using Prometheus, Grafana, OpenTelemetry, or equivalent.
  • BS in Computer Science, Engineering, or related field, or equivalent professional experience.
  • Experience building data catalog, metadata, or lineage systems (preferred).
  • Familiarity with Apache Iceberg or other open table formats (preferred).
  • Experience with CI/CD pipeline design and enforcement gates in large engineering organizations (preferred).
  • Familiarity with AI/ML infrastructure or integrating LLM-powered capabilities into platform services (preferred).
  • MS in Computer Science, Engineering, or related field (preferred).

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