What are the responsibilities and job description for the Analytics Engineer-Apple Services Engineering position at Apple, Inc.?
Would you like to shape how Apple understands and improves iCloud products for hundreds of millions of users worldwide, while having the unique opportunity to impact some of the most far-reaching software applications in the world?
The iCloud Data organization within Apple Services Engineering enables iCloud users to access all their content across apps (Photos, Mail, Messages, FaceTime, Calendar, and more) on every device, all the time, through consistent, scalable, timely, accurate, and fully integrated data infrastructure. We partner closely with product, engineering, and marketing teams to surface insights that drive product decisions, and we're building the analytics foundations that help iCloud continue to grow and delight users at Apple scale.\n\nIf this excites you and you're energized by turning complex data into clear product insights that drive real decisions and by using AI to automate workflows and scale that impact further we'd love to hear from you! We're looking for an exceptional Analytics Engineer with strong data modeling instincts, deep SQL and Python expertise, and the curiosity to embrace AI-native tools and approaches that multiply what a great analyst can accomplish.
4 years of experience in analytics engineering, data engineering, or a closely related analytical role, ideally working with consumer products at scale.\nDeep SQL expertise and proficiency in Python or R for data analysis, transformation, and automation.\nExperience building and maintaining data pipelines and data models using distributed computing frameworks (e.g., Spark, Presto, Trino).\nStrong understanding of data modeling principles, dimensional design, and data architecture best practices that support reliable, scalable analytics.\nExperience defining metrics and partnering with product teams to establish measurement frameworks and reporting.\nComfort working with AI-assisted development tools and an eagerness to apply them to automate workflows, accelerate pipeline development, and scale your analytical output.\nStrong communication skills you translate complex data findings into clear, actionable insights for both technical and non-technical audiences.\nBS or equivalent work experience in a quantitative field (Statistics, Computer Science, Mathematics, or related).
Experience with data visualization tools (e.g., Tableau, Looker, or similar) and building self-serve analytics products for business stakeholders.\nExperience using AI coding assistants and LLM-powered tools to automate analytics workflows, accelerate SQL and pipeline development, and scale analytical output beyond what's possible manually.\nUnderstanding of the data lifecycle and concepts such as data lineage, governance, privacy, retention, and anonymization, particularly in a privacy-sensitive consumer product environment.\nExperience working in a complex, matrixed organization on cross-functional and cross-business projects.\nAdvanced degree in a quantitative field (Statistics, Computer Science, or related).
The iCloud Data organization within Apple Services Engineering enables iCloud users to access all their content across apps (Photos, Mail, Messages, FaceTime, Calendar, and more) on every device, all the time, through consistent, scalable, timely, accurate, and fully integrated data infrastructure. We partner closely with product, engineering, and marketing teams to surface insights that drive product decisions, and we're building the analytics foundations that help iCloud continue to grow and delight users at Apple scale.\n\nIf this excites you and you're energized by turning complex data into clear product insights that drive real decisions and by using AI to automate workflows and scale that impact further we'd love to hear from you! We're looking for an exceptional Analytics Engineer with strong data modeling instincts, deep SQL and Python expertise, and the curiosity to embrace AI-native tools and approaches that multiply what a great analyst can accomplish.
4 years of experience in analytics engineering, data engineering, or a closely related analytical role, ideally working with consumer products at scale.\nDeep SQL expertise and proficiency in Python or R for data analysis, transformation, and automation.\nExperience building and maintaining data pipelines and data models using distributed computing frameworks (e.g., Spark, Presto, Trino).\nStrong understanding of data modeling principles, dimensional design, and data architecture best practices that support reliable, scalable analytics.\nExperience defining metrics and partnering with product teams to establish measurement frameworks and reporting.\nComfort working with AI-assisted development tools and an eagerness to apply them to automate workflows, accelerate pipeline development, and scale your analytical output.\nStrong communication skills you translate complex data findings into clear, actionable insights for both technical and non-technical audiences.\nBS or equivalent work experience in a quantitative field (Statistics, Computer Science, Mathematics, or related).
Experience with data visualization tools (e.g., Tableau, Looker, or similar) and building self-serve analytics products for business stakeholders.\nExperience using AI coding assistants and LLM-powered tools to automate analytics workflows, accelerate SQL and pipeline development, and scale analytical output beyond what's possible manually.\nUnderstanding of the data lifecycle and concepts such as data lineage, governance, privacy, retention, and anonymization, particularly in a privacy-sensitive consumer product environment.\nExperience working in a complex, matrixed organization on cross-functional and cross-business projects.\nAdvanced degree in a quantitative field (Statistics, Computer Science, or related).