Are you ready to make a significant impact at the intersection of data engineering and App Store analytics within Apple? If you are passionate about building robust data pipelines, crafting complex data models, and enabling compelling, data-driven insights at scale, we would love for you to apply! The Apple Services Data Science & Analytics organization drives decisions that improve the customer experience, accelerate growth, and uncover new business opportunities while respecting user privacy and adhering to regulatory policy.
Description
We work on some of the largest e-commerce and media streaming businesses in the world and have an incredible team collaborating on the best ways to improve these services for our customers! Our culture is built on rapid iteration, open debate, and independent thinking — we take calculated risks and work as analytical advisors across product, design, engineering, marketing, editorial, legal, and business teams.
Responsibilities
As a Data Engineer on the Services Data Science & Analytics App Insights Engineering team, you will work with massive internet-scale App Store data across numerous product and customer touchpoints. You will heavily contribute to and maintain the data infrastructure that powers App Store insights — building and optimizing pipelines, designing data models, and ensuring that both DS&A and stakeholder teams have reliable, high-quality data to drive business decisions. A great fit for this role communicates and collaborates well across many different teams, has an agile approach to solving challenging problems quickly, and stresses the details. You will partner closely with insights engineering and data science peers to understand requirements and translate them into scalable data solutions, improving the automation and reliability of core App Store data workflows. Your work will involve building systems that surface patterns across app transactional, subscription, engagement, and commerce data — ensuring consistent, accurate reporting across the organization. Over time, you will help shape how App Store data is structured, governed, and delivered, improving the quality, coverage, and efficiency of our analytical data ecosystem.
Minimum Qualifications
5+ years of experience in data engineering or business intelligence roles, with demonstrated expertise building and maintaining complex data pipelines and data models at scale Strong proficiency with SQL and proven experience querying, manipulating, and analyzing internet-scale data structures and very large datasets Demonstrated experience with data engineering tools and concepts, including pipeline development, data model design, and aggregation strategies in support of analytics and reporting Exceptional written and verbal communication skills, with the ability to translate complex data concepts for diverse audiences including product, business, and engineering stakeholders Strong time management skills with the ability to balance multiple projects with competing priorities BS in Computer Science, Statistics, Mathematics, Information Systems, Engineering, Economics, or a related field
Preferred Qualifications
Experience in a fast-paced technology company, digital subscription business, or large-scale App Store or e-commerce platform, navigating complex data ecosystems Advanced proficiency with Python or similar languages, and experience with distributed computing frameworks (e.g., Spark) for complex data manipulation and analytics Familiarity with data governance, data quality frameworks, or metadata management concepts MS in Computer Science, Statistics, Mathematics, Information Systems, Engineering, Economics, or a related field
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant
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Apple accepts applications to this posting on an ongoing basis.