Sr. Data Engineer - Services Special Projects

Design and operate a real-time data platform that turns continuous multimodal data streams into a trusted foundation for downstream use cases. This onsite role in Cupertino focuses on building scalable batch and streaming pipelines, data governance and observability, and applied ML integration, with a strong emphasis on reliability, performance, and engineering rigor.

Compensation: USD 184,700 to 324,800 per year. The role may also be eligible for discretionary bonuses or commission payments and relocation.

Responsibilities

• Build and implement batch and streaming ETL/ELT pipelines that ingest, process, and model data from diverse sources, including unstructured media and real-time event streams, with high reliability, performance, and scalability.
• Develop and maintain Kafka-based ingestion and processing pipelines to ensure reliable delivery across services into the data lake.
• Create robust logical and physical data models using dimensional modeling, versioning, and storage patterns such as Parquet and ORC for ingest, reporting, and operational use cases.
• Define and enforce data quality checks, SLAs, and observability standards so data stays accurate, timely, versioned, and trusted.
• Integrate and enrich raw signals with metadata and attribution to support downstream use cases including analytics, billing, planning, and optimization.
• Apply standard methodologies for data lineage, metadata management, schema governance, versioning, and security aligned with Apple standards for data protection and privacy.
• Deliver solutions that include logging, anomaly detection, data validation, cleaning, and transformation, with monitoring and debuggability for continuous improvement.
• Partner with ML engineers, data scientists, platform teams, and leadership to translate requirements into scalable, reliable data solutions.
• Advance the team’s data stack, including tooling, frameworks, and standards for development, testing, deployment, 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 schema design optimized for analytics and reporting
• Experience with databases including SQL/NoSQL (Postgres, Cassandra, Redis)
• Strong experience with distributed data processing frameworks such as Apache Spark
• Strong experience with parallel processing frameworks: BigTable and Hadoop
• Strong software engineering fundamentals and proven experience with Scala and Java
• Hands-on experience with Apache Kafka, Apache Iceberg, and Apache 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)
• Experience with analytics frameworks including Trino (Presto, BigQuery, Snowflake)
• Hands-on experience with big data lake architectures
• Experience with containerization and orchestration (Docker, Kubernetes/EKS) and CI/CD tooling including Jenkins
• Experience with Python and PySpark
• Familiarity with graph databases such as TigerGraph
• Experience building pipelines processing multimodal data (structured and image) and integrating ML model inference, including LLMs and embedding models, for data enrichment and transformation
• Hands-on experience deploying, serving, and optimizing LLMs or ML models in production inference runtimes/compilers such as ONNX Runtime and TensorRT (including TensorRT-LLM), plus 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, data security best practices, and data privacy regulations

Preferred Qualifications

• Experience with data versioning tools and frameworks (for example, DVC, Delta Lake)
• Excellent communication skills and a collaborative mindset
• Experience storing and serving embeddings (for example, pgvector, Milvus, FAISS)

Technologies

ETL/ELT, Kafka, Parquet, ORC, dimensional modeling, DVC, Delta Lake, pgvector, Milvus, FAISS, Apache Spark, BigTable, Hadoop, Scala, Java, Apache Iceberg, Apache Flink, Apache Airflow, Beam, AWS, S3, EMR, Lambda, Glue, Redshift, BigQuery, Kinesis, Trino, Presto, Snowflake, Docker, Kubernetes, EKS, Jenkins, Python, PySpark, TigerGraph, LLMs, embedding models, ONNX Runtime, TensorRT, TensorRT-LLM, Triton, vLLM, TorchServe, KV-cache, including Kafka-based ingestion.

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Can purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan
• Role may be eligible for discretionary bonuses or commission payments as well as relocation

Note: Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. Learn more about Apple Benefits.