Software Data Engineer, App Store
Apple
Software Engineering, Data Science
San Francisco, CA, USA
USD 147,400-220,900 / year + Equity
Posted on May 8, 2026
Apple's App Store is the world's largest and most innovative app marketplace, home to over 1.5 million apps and serving more than half a billion customers every week across all Apple devices. Since the App Store launched in 2008, it has changed how we all live: enabling countless new companies, spawning new industries, and building millions of jobs. We believe we are just getting started. Do you have a strong passion for using data to drive business decisions, generate ideas, and inspire collaborators? As a Software Data Engineer on the App Store Data team, you will play an integral role in helping App Store improve the experience for users and developers every day, through analysis grounded in privacy. We build the solutions, services, and analytics that power data-driven innovation for internal stakeholders and external partners. In a world where apps have become essential to daily life, the App Store team has become essential to Apple's business.
As a member of the App Store Data team, you will play a key role in shaping our strategic direction and driving high-impact initiatives. The data we produce informs Apple Leadership and partners, helping guide decisions on innovation and emerging opportunities. Success in this role requires a strong commitment to building world-class analytical solutions. You will build the design and delivery of compliance-critical data products, including pipelines and analytical outputs that meet regulatory and privacy requirements. This role also encompasses analytics engineering, exploratory data analysis, ML-driven data observability and developing scalable self-service data products.
- Design and build scalable data pipelines with CI/CD ensuring reliability with unit/integration tests
- Develop data architectures using distributed systems (e.g., Spark, Kafka) and optimize for high-throughput processing
- Implement monitoring, observability, and automated recovery for data systems in production
- Clean, transform, and model large scale data into analytics-ready datasets using Python, Scala or Java
- Contribute to large-scale quantitative analysis projects through all phases; this includes data quality, data modeling, algorithm/feature development, statistical analysis, and data visualization
- Collaborate with stakeholders from Engineering, Product, Legal, Privacy, and Governance to translate requirements into precise data model specifications and data analytics product
- Use AI coding assistants for architecture exploration, pipeline generation, test coverage, and documentation as standard practice. Treat AI-generated code with the same review rigor as human-authored code
- 3+ years of experience building production data pipelines on distributed systems
- Solid programming skills in Scala or Java or Python and proficiency in SQL
- Hands-on experience in big data technologies, such as: Hadoop, Spark/Flink, Kafka, Airflow, Iceberg, Trino and Kubernetes
- Working knowledge of data modeling fundamentals, structured and unstructured data analysis, predictive modeling techniques to identify patterns, translating domain requirements into engineering specifications
- Active use of AI coding assistant, actively learning LLM-augmented workflows
- Excellent communication; proven success partnering with cross‑functional stakeholders (Product, Engineering, Legal, Privacy etc.)
- Bachelor's Degree in Computer Science, Engineering or a Quantitative discipline (e.g., Statistics, Economics, Mathematics).
- Exposure to privacy, compliance, or governance data requirements in a pipeline or analytics context
- Exposure to ML or statistical anomaly detection, even in a learning or project context
- Experience in writing and maintaining high-quality code using standard methodologies such as code reviews, unit testing, and continuous integration.