The ASE Content Data Engineering team is responsible for the internal business intelligence infrastructure that powers Apple's online media store. The data models and reports developed by this team supply information to engineering, business operations, and external content providers regarding the status of online media, including workflow status, availability, and incoming and outgoing volume.
This engineer will own and evolve a large portfolio of data pipelines and reporting systems, driving the ongoing migration from legacy infrastructure to modern pipeline orchestration and cloud-based storage, while also delivering net-new reports and data models in response to business, engineering, and regulatory needs. The role demands a blend of engineering rigor, data fluency, and a strong reliability mindset: the team's reports directly support legal and compliance obligations with hard, externally-imposed deadlines.
The ideal candidate brings a bias toward automation and self-healing pipelines over manual intervention, and is comfortable managing a broad portfolio of concurrent work, balancing urgent pipeline fixes against longer-horizon migration and enhancement projects. They can independently troubleshoot and resolve ambiguous, production-impacting issues under time pressure, and communicate technical findings clearly to non-engineering stakeholders across legal, business operations, and content provider teams. Knowledge of or genuine curiosity about online media is a plus.
Minimum Qualifications
Minimum 1 year of experience as a Software Engineer, Data Engineer, or equivalent role
Strong experience with Spark and Spark SQL, including performance tuning and resource optimization
Strong experience with Hadoop, HDFS, and/or S3-compatible object storage
Experience writing SQL and PL/SQL on Oracle, including query optimization
Experience with Flink or Flink SQL for stream-based data processing
Experience with Java and/or Scala development
Experience building and maintaining automated data pipelines and job scheduling systems (e.g. Airflow)
Experience with CI/CD workflows and version-controlled deployment of data pipeline code
Hands-on experience with Kubernetes for container-based workload execution
Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field OR Hands-on engineering experience and a demonstrable track record of building and maintaining production data systems are weighted equally alongside formal academic credentials.
Preferred Qualifications
Familiarity with Iceberg / Parquet-based table formats
Proficiency in macOS and Linux server environments
Experience using GenAI tools (e.g., Claude, OpenAI tools) to accelerate engineering tasks
Familiarity with cloud object storage migration patterns (HDFS to S3)
Experience with EKS or other cloud-based compute platforms
Familiarity with Trino or other distributed SQL query engines
Familiarity with GenAI-assisted data access patterns (e.g., MCP servers, Text-to-SQL, LLM-assisted data documentation)
Familiarity with Tableau or similar data visualization and dashboarding tools
Experience with shell scripting for automation and pipeline support
Hands-on experience with Python scripting for data processing and automation