Sr. Data Engineer - Services Special Projects

Apple • Cupertino, CA

1d

About The Position

Apple is building a large-scale, real-time platform that transforms continuous streams of multimodal data—including structured, image, and log data—into an intelligent, searchable foundation. The platform enriches data with language and embedding models to support downstream applications and experiences for Apple customers. The Sr. Data Engineer will design, build, and operate the infrastructure and scalable pipelines that turn raw data into a trusted foundation for decision-making and operations.

Requirements

• Master's degree.
• 10+ years of experience in data engineering, including building and maintaining large-scale ETL/ELT pipelines.
• Proficiency in data modeling, especially dimensional modeling, and in designing schemas for analytics and reporting.
• Experience with SQL and NoSQL databases, including PostgreSQL, Cassandra, and Redis.
• Strong experience with distributed data processing frameworks, including Apache Spark.
• Strong experience with parallel processing frameworks such as BigTable and Hadoop.
• Strong software engineering fundamentals and 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, BigQuery, and Kinesis, or similar services.
• Experience with analytics frameworks such as Trino, Presto, BigQuery, or Snowflake.
• Hands-on experience with big data lake architectures.
• Experience with containerization and orchestration using Docker and Kubernetes/EKS, and CI/CD tooling including Jenkins.
• Experience with Python and PySpark.
• Experience building pipelines that process multimodal data, including structured and image data, and integrate ML model inference, including LLMs and embedding models, for enrichment and transformation.
• Hands-on experience deploying, serving, and optimizing LLMs or ML models in production, including inference runtimes or compilers such as ONNX Runtime and TensorRT/TensorRT-LLM, and serving frameworks such as Triton, vLLM, TorchServe, or similar.
• Experience tuning batching, KV-cache, and GPU utilization for low-latency, high-throughput real-time inference in a data pipeline.
• Knowledge of data governance principles, data security best practices, 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 or serving embeddings using tools such as pgvector, Milvus, or FAISS.

Nice To Haves

• Familiarity with graph databases such as TigerGraph.

Responsibilities

• Build and implement batch and streaming ETL/ELT pipelines to ingest, process, and model diverse data, including unstructured media and real-time event streams.
• Develop and maintain Kafka-based ingestion and processing pipelines for reliable data delivery across services and into the data lake.
• Build logical and physical data models, emphasizing dimensional modeling, versioning, and storage patterns such as Parquet and ORC.
• Define and enforce data quality checks, SLAs, and observability standards so data is accurate, timely, versioned, and trusted.
• Integrate raw signals with metadata and attribution to support analytics, billing, planning, and optimization.
• Implement data lineage, metadata management, schema governance, versioning, and security practices aligned with Apple's data protection and privacy standards.
• Deliver logging, anomaly detection, data validation, cleaning, and transformation capabilities, emphasizing monitoring, debuggability, and continuous improvement.
• Collaborate with ML engineers, data scientists, platform teams, and leadership to translate requirements into scalable, reliable data solutions.
• Help advance the team's data stack, including tooling, frameworks, and standards for development, testing, deployment, and operations.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior