Data Engineer, Apple Ads

Apple•Austin, TX

About The Position

At Apple, we build products and services that enrich people’s lives. Apple Ads helps customers discover relevant products and content while enabling developers, publishers, and advertisers to grow their businesses. Privacy is fundamental to how we design and build our advertising platform. The Apple Ads engineering organization operates large-scale data systems that process and transform high-volume advertising events into reliable datasets and products used for reporting, measurement, analytics, and other critical business functions. We are looking for a Software Engineer with strong software engineering fundamentals and experience building large-scale distributed data processing systems. In this role, you will design, develop, and operate production data pipelines using technologies such as Apache Spark, Kafka, Java/Scala, cloud storage, and modern data lake technologies. You will work on challenging problems involving large-scale batch and streaming data processing, data correctness, privacy, reliability, scalability, and performance. You will have opportunities to own systems end-to-end—from architecture and implementation through deployment, observability, and production support. As a Software Engineer on the Apple Ads data engineering team, you will help build the next generation of scalable data processing and reporting infrastructure. You will design and implement distributed data pipelines that process large volumes of advertising events across batch, near-real-time, and streaming execution environments. You will work on systems where correctness, data quality, performance, reliability, and privacy are critical.

Requirements

3+ years of professional software engineering or data engineering experience building production systems.
Strong computer science fundamentals, including data structures, algorithms, concurrency, and distributed systems concepts.
Strong programming skills in Java and/or Scala, with experience writing production-quality software.
Hands-on experience building and operating large-scale data pipelines using Apache Spark.
Strong understanding of Spark concepts including partitioning, shuffles, joins, caching, execution plans, memory management, and performance tuning.
Experience designing distributed batch and/or streaming data processing systems.
Experience with technologies such as Kafka, Hadoop, S3/object storage, or equivalent large-scale data infrastructure.
Strong SQL skills and experience working with large analytical datasets.
Expertise in distributed systems and data processing technologies (e.g. Spark, Kafka, Flink)
Understanding of data modeling, partitioning strategies, schema evolution, and efficient storage formats such as Parquet.
Experience building reliable production systems with appropriate testing, monitoring, alerting, and operational support.
Strong debugging and problem-solving skills, particularly across complex distributed systems.
Ability to communicate effectively and collaborate with technical and non-technical cross-functional partners.
Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

Experience designing and operating petabyte-scale data processing systems.
Deep expertise in Apache Spark performance tuning and optimization.
Experience with Apache Iceberg or similar modern data lake/lakehouse technologies.
Experience with both batch and real-time/streaming architectures, including Kafka and/or Flink.
Experience building data platforms or processing frameworks that are reused by multiple teams or pipelines.
Experience with AWS technologies, including S3 and Kubernetes/EKS or equivalent cloud platforms.
Experience running distributed workloads using Kubernetes and containerized environments.
Familiarity with analytical data stores and query engines such as Druid, Trino, or similar technologies.
Experience designing systems that support replay, backfills, reprocessing, and late-arriving data.
Experience implementing data-quality, reconciliation, lineage, or data-contract frameworks.
Understanding of privacy-preserving data processing and secure handling of large-scale datasets.
Experience building systems for advertising, measurement, reporting, or analytics.
Demonstrated ability to take ownership of complex projects and drive them from design through production.

Responsibilities

Design, develop, and operate large-scale distributed data processing pipelines using Apache Spark and related big-data technologies.
Build reliable batch, near-real-time, and streaming pipelines for processing high-volume advertising and measurement data.
Develop production-quality software primarily using Java, Scala, and/or Python.
Design scalable data architectures for processing and storing very large datasets.
Build pipelines using technologies such as Spark, Kafka, S3/object storage, Apache Iceberg, Hadoop, and cloud-native infrastructure.
Develop efficient data transformations, aggregations, joins, and data-processing algorithms over large datasets.
Analyze and optimize Spark applications for performance, memory utilization, shuffle efficiency, parallelism, and compute cost.
Design systems that gracefully handle late-arriving data, retries, partial failures, reprocessing, and evolving data schemas.
Build strong data-quality controls, validation mechanisms, reconciliation frameworks, and monitoring to ensure correctness throughout the data lifecycle.
Design systems with privacy, security, and appropriate data-handling principles built into the architecture.
Develop observability, metrics, alerting, and debugging capabilities for production data pipelines.
Investigate and resolve complex issues across distributed compute, storage, orchestration, and downstream data systems.
Participate in architecture and design reviews and contribute to technical decisions for evolving the data platform.
Write clean, maintainable, well-tested code and participate actively in code reviews.
Own services and pipelines through their complete lifecycle, including design, development, deployment, monitoring, and production support.
Collaborate with cross-functional engineering and product teams to deliver scalable solutions for Apple Ads.