SUMMARY

Posted: Mar 20, 2025
Weekly Hours: 40
Role Number:200595736
Are you ready to make a significant impact on Apple Services? At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly! If you are passionate about data engineering and analytics, we would love for you to apply! The Apple Services Data Science & Analytics team drives decisions that improve the customer experience, accelerate growth, and uncover new business opportunities, all while respecting user privacy. We work on some of the largest e-commerce and media streaming businesses in the world, and our incredible team collaborates to find the best ways to enhance these services for our customers. Our team collaborates as the analytics team with partners across product, design, engineering, marketing, editorial, and business teams. The Data Engineering team within Services Data Science & Analytics focuses on solving the challenges inherent in producing consistent and reliable analytics from massive datasets across a wide range of services. We create and maintain data pipelines and a data lake to process and store data in a structure that enables fast and flexible analytics products.

DESCRIPTION

As a Data Engineer on the Services Data Science & Analytics team, you will be responsible for designing, developing, and maintaining robust data pipelines to support Apple Services analytics initiatives. Our team’s task is to build a comprehensive aggregate data layer that enables efficient and flexible executive reporting, highly customized data applications, and powerful ML inference and analysis. In this role, you will work closely with data scientists, BI engineers, and business and product teams to build scalable data pipelines and solutions. As a data engineer, you must effectively collaborate to bridge the gap between business needs, analytical solutions, and engineering requirements. Additionally, proactive collaboration with other data engineering teams is essential for scaling solutions across teams and Apple Services. This role requires expertise in data engineering tools and patterns, including developing PySpark jobs, using orchestration tools for scheduling and monitoring, understanding CI/CD processes, and managing a dynamic data lake. The team maintains pipelines that require on-call support and monitoring. A successful engineer will have a strong intuition for quickly identifying and resolving bugs and efficiently solving technical challenges. Responsibilities: * Independently design technical solutions to process massive datasets (billions of daily records) and unify analytics across Apple Services * Build data pipelines using Python, PySpark and SQL * Manage an evolving data schema ensuring that data structures can extend over time while also maintaining backward compatibility * Collaborate with Business Intelligence and Data Science teams to design aggregate tables used across multiple teams and workflows * Integrate new tools and packages into the team’s workflow and codebase * Provide on-call support and monitoring * Mentor and provide data engineering best practices across the organization

MINIMUM QUALIFICATIONS

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• 5+ years of experience as a Data Engineer or similar role

• 5+ years of experience with data schema design and data modeling
• 5+ years of experience in Python and SQL
• 2+ years of Spark/PySpark experience
• Business Intelligence experience - aggregated data sources, metric definition, data visualization with tools like Tableau
• Strong critical thinking, debugging, and technical design skills
• Bachelors degree in Computer Science, Statistics, Mathematics, Engineering, Information Systems, Economics or related field

PREFERRED QUALIFICATIONS

• Deep experience in Spark
• Experience in digital subscriptions, media, and/or app-based engagement data
• Experience in Data Governance & User Privacy
• Experience with container orchestration and deployment frameworks (eg. Kubernetes, Docker)
• Data Platform engineering experience to support deployment of pipelines across many diverse teams
• Masters degree in Computer Science, Statistics, Mathematics, Engineering, Information Systems, Economics or related field

Please refer the Job description for details