Data Engineer - Scalable Ads Data Pipelines

Apple Inc.; Cupertino (CA); Hybrid; USD 150,000 - 278,000; Full time

Job summary

Apple Inc. is seeking a Software Engineer to design, build, and operate large-scale distributed data processing pipelines for Apple Ads.

You will develop production-grade software using Java/Scala/Python, optimize Spark workloads, and ensure data correctness, privacy, and reliability across batch, near-real-time, and streaming environments. You will collaborate with cross-functional teams to translate requirements into scalable data solutions, own end-to-end pipelines, and contribute to

Qualifications

• 3+ years of professional software engineering or data engineering experience building production systems.
• Strong CS fundamentals in data structures, algorithms, concurrency, and distributed systems.
• Strong programming skills in Java and/or Scala with production-quality software experience.
• Hands-on experience building and operating large-scale data pipelines using Apache Spark.
• Strong Spark concepts: partitioning, shuffles, joins, caching, memory management, performance tuning.
• Experience with Kafka, Hadoop, S3/object storage, or equivalent large-scale data infra.
• Strong SQL skills with large analytical datasets.
• Distributed systems and data processing tech (e.g. Spark, Kafka, Flink).
• Data modeling, partitioning strategies, schema evolution, Parquet storage formats.
• Experience building reliable production systems with testing, monitoring, and ops support.
• Strong debugging and problem-solving across distributed systems.
• Ability to communicate with technical and non-technical partners.
• Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or related field, or equivalent practical experience.

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 Spark, Kafka, S3/object storage, Apache Iceberg, Hadoop, and cloud-native infra.
• 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 handle late-arriving data, retries, partial failures, reprocessing, and evolving schemas.
• Build data-quality controls, validation, reconciliation, and monitoring for data life-cycle correctness.
• Design systems with privacy, security, and proper data-handling principles.
• Develop observability, metrics, alerting, and debugging for production data pipelines.
• Investigate and resolve issues across distributed compute, storage, orchestration, and downstream data systems.
• Participate in architecture/design reviews and contribute to decisions for evolving the data platform.
• Write clean, maintainable, well-tested code and participate in code reviews.
• Own services/pipelines through design, development, deployment, monitoring, and production support.
• Collaborate with cross-functional teams to deliver scalable Apple Ads data solutions.

Skills

Java

Scala

Python

Distributed systems

Spark

Kafka

SQL

Data modeling

Problem solving

Communication

Education

Bachelor's degree in CS or related field

Tools

Apache Spark

Hadoop

Iceberg

Kubernetes

AWS S3

Flink

Job description

Apple Inc. is seeking a Software Engineer to design, build, and operate large-scale distributed data processing pipelines for Apple Ads.

You will develop production-grade software using Java/Scala/Python, optimize Spark workloads, and ensure data correctness, privacy, and reliability across batch, near-real-time, and streaming environments. You will collaborate with cross-functional teams to translate requirements into scalable data solutions, own end-to-end pipelines, and contribute to