Data Engineer, Apple Ads

Responsibilities

• Design and implement distributed data pipelines processing large volumes of advertising events across batch, near-real-time, and streaming environments.
• Build, deploy, operate, and provide production support for scalable data processing and reporting infrastructure.
• Optimize Apache Spark workloads, including partitioning, shuffles, joins, caching, execution plans, memory management, and performance.
• Design data models, partitioning strategies, schema evolution approaches, and efficient storage solutions.
• Ensure data correctness, quality, reliability, privacy, scalability, and performance across production systems.
• Build systems supporting replay, backfills, reprocessing, late-arriving data, reconciliation, lineage, and data contracts.
• Collaborate with engineers, product teams, data scientists, SREs, and technical and non-technical partners.
• Own complex projects end-to-end from architecture and implementation through deployment, observability, monitoring, alerting, and operational support.

Requirements

• At least 3 years of professional software engineering or data engineering experience building production systems.
• Strong computer science fundamentals in data structures, algorithms, concurrency, and distributed systems.
• Strong production programming skills in Java and/or Scala.
• Hands-on experience building and operating large-scale data pipelines with Apache Spark.
• Experience designing distributed batch and/or streaming data processing systems.
• Experience with large-scale data infrastructure such as Kafka, Hadoop, and S3 or equivalent technologies.
• Strong SQL skills and experience working with large analytical datasets.
• Knowledge of distributed data processing technologies such as Spark, Kafka, and Flink.
• Understanding of data modeling, partitioning strategies, schema evolution, and efficient storage formats such as Parquet.
• Experience building reliable production systems with testing, monitoring, alerting, and operational support.
• Strong debugging, problem-solving, communication, and cross-functional collaboration skills.
• Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.
• Preferred experience with petabyte-scale data processing systems and deep Apache Spark performance tuning.
• Preferred experience with Apache Iceberg, AWS technologies including S3, Kubernetes/EKS, and containerized distributed workloads.
• Preferred familiarity with analytical data stores and query engines such as Druid and Trino.
• Preferred experience building reusable data platforms or processing frameworks and privacy-preserving data systems.
• Preferred experience with advertising, measurement, reporting, analytics, data-quality, reconciliation, lineage, or data-contract frameworks.