Apple
Data Products & Business Intelligence Lead - Wallet, Payments & Commerce
$216K–$325K
Austin, Texas +2 · Full-time · Posted Sep 17, 2026
- Location
- Austin, Texas
Cary, North Carolina
Cupertino, California - Experience
- 10+ years in data and AI-powered analytical products; 3+ years as a senior IC leading data products, AI-enabled analytics, and visualizations across multiple lines of business.
- Education
- Bachelor’s or Master’s degree in Computer Science or a related technical field or equivalent experience.
About the job
Leads the design and delivery of scalable, privacy-first analytics data products, metrics, and visualizations for Apple Wallet, Payments & Commerce.
What you'll do
- Define instrumentation and user journeys, and build and maintain analytical datasets, data products, and metrics layers.
- Establish data quality frameworks and develop visualizations and decision tools, including AI-enabled analytics.
- Gather and translate partner requirements into product specifications, lead discovery workshops, and mentor junior team members.
What they ask for
- Bachelor’s or Master’s degree in Computer Science or a related technical field, or equivalent experience.
- 10+ years designing, developing, and deploying data and AI-powered analytical products; 3+ years as a senior IC leading data products, AI-enabled analytics, and visualizations across multiple lines of business.
- Strong proficiency in SQL, Scala, and Python; hands-on experience with data pipeline tools, CI/CD practices, and version control.
- Experience with Generative AI/LLMs and agentic AI patterns for analytics; strong understanding of data warehousing, dimensional modeling, semantic/metrics layers, and metric standardization.
Nice to have
- Familiarity with digital payments or fintech data and AI-driven analytics or conversational BI.
- Knowledge of data governance, data cataloging, and metadata management.
Pay
$216,200 and $324,800 USD base salary
Tools
SQL, Scala, Python, Apache Spark, Kafka, Airflow, Generative AI/LLMs
AI focus
Applies Generative AI/LLMs and agentic AI patterns to analytics, including insight generation, natural-language data exploration, workflow automation, and AI-driven inferences and visualizations.
Model-written summary · the original posting is authoritative