Senior Product Manager, Applied Analytics, Data Engineering & AI/GenAI Platforms

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Cupertino, CA
Salary
$207,400–$372,700 / yr
Posted
2 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $203k
This role $290k
$141k most similar roles pay here $398k

This role pays more than 97% of similar roles. Most pay $177,250–$228,112 — the shaded band above. At the midpoint, this role pays about $290k versus about $203k 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 2299 open roles on FindRole.

Listed pay typically runs $175,000–$277,600 across 1870 roles with salary data.

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At a glance

TL;DR · Senior Product Manager, Applied Analytics, Data Engineering & AI/GenAI Platforms

Senior Product Manager – Applied Analytics, Data Engineering & AI/GenAI Platforms joins the team to lead application-layer data and AI capabilities built upon the core data platform. This role focuses on transforming foundational infrastructure into developer-facing products, intelligent services, and GenAI-enabled workflows for data creation, transformation, governance, and analysis. You will define product visions, shape multi-quarter roadmaps, and deliver APIs, automated reasoning workflows, and tools to help developers build scalable applications. The position requires expertise in analytics, data engineering systems, large-scale distributed infrastructure, and modern AI stacks including cloud-based platforms. Key responsibilities involve translating complex technical requirements into clear abstractions while balancing platform constraints like privacy, security, and reliability. You will solve problems related to data pipeline optimization and the integration of GenAI features into internal workflows for various service areas.

What does a Product Manager earn in California?

Median $222375 from 160 postings across 38 companies.

See salary data

What you'll do

  • Define and own the product vision and multi-quarter roadmap for application-layer data and AI capabilities.
  • Translate foundational data infrastructure into developer-facing products such as APIs, workflows, and templates.
  • Develop AI and GenAI features including intelligent assistants and automated reasoning workflows for data engineering.
  • Design tools that enable developers to build data-driven and AI-native applications at scale.
  • Align product requirements with platform constraints regarding scalability, reliability, privacy, and security.
  • Identify and address the real-world needs of internal customer teams to co-develop technical solutions.
  • Communicate complex technical trade-offs clearly to both technical and non-technical stakeholders.
  • Measure success through metrics such as adoption rates, developer productivity, and system reliability.

What we're looking for

  • Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, or a related technical field (Master's degree preferred).
  • 10+ years of product management experience building enterprise or platform products.
  • Experience working with analytics, data engineering systems, data platforms, or large-scale distributed infrastructure.
  • Demonstrated ability to take products from concept to launch, including vision setting and roadmap ownership.
  • Working knowledge of modern data and AI stacks, including cloud-based data platforms and analytics systems.
  • Deep understanding of data engineering concepts like ingestion pipelines, transformations, metadata, lineage, and governance (preferred).
  • Experience building or productizing AI/ML or GenAI-powered features (preferred).
  • Familiarity with public cloud platforms and enterprise-scale deployments (preferred).

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