Description:

Apple is hiring a Data Platform Engineer to join its global Apple Ads Data Platform squad in Hyderabad. This team architects the foundation for data ingestion, generic streaming transformations, and data privacy governance across high-profile monetization engines—including Search Ads, App Store, and Apple News advertising ecosystems. Moving beyond static pipeline building, you will build and operate multi-tenant managed data services that process massive, high-throughput event streams under strict privacy guardrails. You will take ownership of technical initiatives from initial concept through production release, build scalable APIs, perform deep diagnostics on high-concurrency clusters, and provide mentorship to junior engineering peers.

Key Technologies:

Apache Spark, Apache Flink, Kafka / Kafka Connect, Apache Iceberg, Apache Hive, Java, Scala, Rust, Kubernetes, Apache Hadoop YARN, REST APIs, Pub/Sub Messaging

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or a related quantitative technical discipline is required; a Master's or Ph.D. in Computer Science is strongly preferred.
• 3 to 7 years of professional software engineering experience designing, scaling, and maintaining large-scale, production-critical distributed systems.
• Advanced, hands-on programming expertise running massive distributed computation layers using Apache Spark and real-time streaming engines like Apache Flink.
• Strong software engineering fundamentals in Java or Scala. Hands-on system engineering exposure to Rust is a major plus.
• Practical backend experience managing open-source table formats and catalog architectures, specifically Apache Iceberg and Apache Hive.
• Solid understanding of building Pub/Sub messaging frameworks and data ingestion adapters using Kafka and Kafka Connect.
• Operational familiarity deploying and managing containerized or distributed computing tasks over Kubernetes or Hadoop YARN.
• Proven technical expertise designing, implementation-hardening, and maintaining scalable, reliable interface APIs for internal developer ecosystems.
• Highly skilled at isolating, debugging, and tracing complex concurrency bottlenecks or state issues inside highly distributed, multi-node compute setups.
• Active code contributions to open-source big data projects (Iceberg, Spark, Kafka, Flink) or secondary baseline exposure to applied machine learning, automated telemetry analytics, and business intelligence (BI) systems.