below marketmost similar roles pay hereabove market
This role pays more than
98%
of similar roles. Most pay
$177,250–$224,897
— the blue band above.
At the midpoint, this role pays about
$290k
versus about
$201k
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
3552 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2753 roles with salary data.
The Senior Product Manager – Applied Analytics, Data Engineering & AI/GenAI Platforms joins the team responsible for building next-generation applied data and AI platforms. This role focuses on transforming foundational data infrastructure into developer-facing products, intelligent services, and GenAI-enabled workflows. You will own product capabilities that sit above the core data platform, designing application-layer data services, APIs, and platform intelligence to help developers troubleshoot and optimize data pipelines. Key responsibilities include leading the development of AI and GenAI-powered experiences for data creation, transformation, governance, and analysis. You will utilize a modern data and AI stack, including cloud-based platforms and analytics systems, to solve complex problems regarding ingestion pipelines, batch and streaming systems, metadata, lineage, and governance while ensuring platform scalability, reliability, and security.
What does a Product Manager earn in California?
Median $222500 from 313 postings across 50 companies.
Define and own the product vision and multi-quarter roadmap for application-layer data and AI capabilities.
Translate foundational data and AI infrastructure into clear product abstractions like APIs, workflows, and templates.
Lead the development of applied AI and GenAI features for data engineering and analytics use cases.
Partner with engineering teams to align product requirements with platform architecture, scalability, and security constraints.
Co-develop solutions with internal customer teams to address real-world application needs.
Communicate complex technical tradeoffs clearly to both technical and non-technical stakeholders.
Drive prioritization across competing demands from multiple organizations while maintaining platform consistency.
Measure product success through adoption, developer productivity, reliability, and customer impact.
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.
Strong 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, roadmap ownership, and execution.
Working knowledge of modern data and AI stacks, including cloud-based data platforms and analytics systems.
Deep understanding of data engineering concepts such as ingestion pipelines, transformations, batch and streaming systems, metadata, lineage, and governance (preferred).
Experience building or productizing AI/ML or GenAI-powered features (preferred).
Generative AI
Machine Learning
APIs
Data Modeling
Data Governance
Snowflake
Databricks
Microsoft Azure
Amazon Web Services
Google Cloud
Data Pipelines
Knowledge Graphs
Retrieval Architectures
Large Language Models
Data Quality
Observability
Generative AI
Machine Learning
APIs
Data Modeling
Data Governance
Snowflake
Databricks
Microsoft Azure
Amazon Web Services
Google Cloud
Data Pipelines
Knowledge Graphs
Retrieval Architectures
Data Quality
Observability
South San Francisco, CA47 days ago
$140,300–$260,500
AI
ML
SQL
AWS
GCP
Azure
Big Data
Data Mesh
Data Fabric
Data Governance
Data Quality
Data Observability
Informatica Data Quality
Collibra
Monte Carlo
Agile
Data Lifecycle Management
FAIR Data Principles
Semantic Layer