Senior Software Data Engineer, App Store
Apple
Software Engineering, Data Science
San Francisco, CA, USA
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 Senior 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 lead 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.
- Lead 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
- 7+ years of experience building production grade distributed data pipeline and data systems
- Strong programming skills in Scala or Java, proficient in Python and SQL with excellent analytical and problem-solving skills
- Hands-on experience in big data technologies such as: Hadoop, Spark/Flink, Kafka, Airflow, Iceberg, Trino and Kubernetes
- Proficiency in data modeling fundamentals, structured and unstructured data analysis, predictive modeling techniques to identify patterns, translate domain requirements into engineering specifications
- Active daily use of AI coding assistants, comfortable with 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)
- Ability to capture cross-functional requirements and translate them into practical data analysis solutions that drive key product decisions
- Experience with ML-based or statistical anomaly detection with exposure to agentic workflow frameworks applied to pipeline generation or monitoring
- Experience with data visualization and reporting tools such as Superset, Tableau
- Experience with Regulatory and Compliance engineering with GDPR operational requirements, data minimization, DMA, DSA obligations and Privacy-first data architecture at a technical level
- Experience working with privacy, legal and regulatory stakeholders in an engineering capacity