Apple is seeking an experienced Data Engineer to join their team focused on building a massive, real-time platform that transforms multimodal data into an intelligent, searchable foundation. The role involves designing, building, and operating scalable ETL/ELT pipelines that support critical decision-making for billions of customers. The ideal candidate will have deep expertise in data architecture and applied ML pipelines. This position offers the opportunity to work with cutting-edge technologies and contribute to innovative customer experiences.

• Masters Degree in a relevant field.
• 10+ years of experience in data engineering, including building and maintaining large-scale ETL/ELT data pipelines.
• Proficiency in data modeling, especially dimensional modeling, and designing schemas optimized for analytics and reporting.
• Experience with SQL/NoSQL databases including Postgres, Cassandra, and Redis.
• Strong experience with distributed data processing frameworks including Apache Spark.
• Strong experience with parallel processing frameworks such as BigTable and Hadoop.
• Proven experience with Scala and Java.
• Hands-on experience with Apache Kafka, Iceberg, and Flink.
• Experience with workflow orchestration tools including Apache Airflow and Beam.
• Experience with AWS services such as S3, EMR, Lambda, Glue, Redshift, and Kinesis.
• Experience with analytics frameworks including Trino, BigQuery, and Snowflake.
• Hands-on experience with big data lake architectures.
• Experience with containerization and orchestration tools like Docker and Kubernetes/EKS.
• Experience with CI/CD tooling including Jenkins.
• Experience in Python and PySpark.
• Familiarity with graph databases such as TigerGraph.
• Experience building pipelines that process multimodal data and integrate ML model inference.
• Hands-on experience deploying and optimizing LLMs or ML models in production.
• Experience tuning batching, KV-cache, and GPU utilization for real-time inference.
• Knowledge of data governance principles and data privacy regulations.
• Experience with data versioning tools and frameworks such as DVC and Delta Lake.
• Excellent communication skills and a collaborative mindset.
• Experience storing and serving embeddings using tools like pgvector, Milvus, and FAISS.

• Design, build, and operate scalable ETL/ELT pipelines for multimodal data.
• Create a trusted data foundation that drives decision-making across the system.
• Collaborate with cross-functional teams to enhance data processing capabilities.
• Integrate ML model inference for data enrichment and transformation.
• Optimize data pipelines for low-latency and high-throughput performance.
• Ensure compliance with data governance and security best practices.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.