Data Engineer, Apple Ads

Socket.dev; Austin (TX); On-site; USD 130,000 - 190,000; Full time

Job summary

Apple Ads data engineering team seeks a Software Engineer to build and operate production data pipelines using Spark, Kafka, and cloud storage. You will design distributed pipelines for batch, near-real-time, and streaming workloads with attention to correctness, privacy, reliability, and performance.

The ideal candidate has hands-on Spark experience, strong CS fundamentals, and a track record of delivering scalable data infrastructure.

Qualifications

• 3+ years in professional software or data engineering on production systems.
• Strong CS fundamentals: data structures, algorithms, concurrency, distributed systems.
• Production-quality Java and/or Scala software development.
• Hands-on with large-scale data pipelines using Apache Spark.
• Deep Spark knowledge: partitioning, shuffles, joins, caching, memory, performance.
• Experience designing distributed batch/streaming systems with Kafka/Hadoop/S3.
• Strong SQL skills on large analytical datasets.
• Knowledge of data modeling, partitioning, schema evolution, Parquet formats.
• Experience building reliable production systems with testing, monitoring, and ops.

Responsibilities

• Design, develop, and operate production data pipelines at scale.
• Own end-to-end data solutions from architecture to deployment and observability.
• Collaborate with engineers, data scientists, SREs, and product teams to translate requirements.

Skills

Java

Scala

Distributed systems

Spark

Kafka

SQL

Data modeling

Troubleshooting

Education

Bachelor's degree in CS/CE/SE or equivalent

Tools

Apache Spark

Kafka

Hadoop

S3/ object storage

Iceberg

Kubernetes

Job description

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.

Description

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. The ideal candidate is a strong software engineer who also has hands-on experience with Apache Spark and large-scale data processing. You should be comfortable reasoning about distributed systems, debugging complex data pipelines, optimizing Spark workloads, designing data models, and building production-quality software around big-data processing frameworks. You will collaborate with engineers, product teams, data scientists, SREs, and other cross-functional partners to translate business and technical requirements into scalable data solutions.

Minimum Qualifications

• 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.

Preferred Qualifications

• 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.