Sr. Data Engineer - Services Special Projects

Apple is building and operating large-scale systems that turn continuous, multimodal data streams into trusted foundations for downstream work. In this onsite role in Cupertino, you will design, build, and run real-time and batch data engineering solutions, combining ETL/ELT with applied machine learning pipelines that support ingestion, modeling, governance, and inference-driven enrichment.

The Senior Data Engineer - Services Special Projects position focuses on scalable ingestion and processing, logical and physical data modeling, and end-to-end reliability for a platform handling diverse sources, including unstructured media and real-time event streams.

What you’ll do

• Build and implement batch and streaming ETL/ELT pipelines to ingest, process, and model data from diverse sources, including unstructured media and real-time event streams, with a focus on reliability, performance, and scalability.
• Develop and maintain Kafka-based ingestion and processing pipelines to support reliable data delivery across services and into the data lake.
• Create robust logical and physical data models with dimensional modeling, versioning, and storage patterns optimized for ingest, reporting, and operational use cases (for example, Parquet and ORC).
• Define and enforce data quality checks, SLAs, and observability standards so data is accurate, timely, versioned, and trusted by stakeholders.
• Integrate and enrich raw signals with metadata and attribution to power downstream use cases such as analytics, billing, planning, and optimization.
• Implement standard data lineage, metadata management, schema governance, versioning, and security aligned with Apple standards for data protection and privacy.
• Deliver solutions featuring logging, anomaly detection, data validation, cleaning, and transformation, with an emphasis on monitoring, debuggability, and continuous improvement.
• Collaborate with ML engineers, data scientists, platform teams, and leadership to translate requirements into scalable and reliable data solutions.
• Advance the team’s data stack, including tooling and frameworks for development, testing, deployment, and operations.

Minimum 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, with schema design optimized for analytics and reporting
• Experience with databases including SQL/NoSQL (including Postgres, Cassandra, Redis)
• Strong experience with distributed processing frameworks such as Apache Spark
• Strong experience with parallel processing frameworks including BigTable/Hadoop
• Strong software engineering fundamentals with proven experience using 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, Kinesis, or similar services
• 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 that process multimodal data (structured and image) 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 inference runtimes/compilers (ONNX Runtime, TensorRT, TensorRT-LLM) and serving frameworks (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

Technologies you’ll work with

• 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
• Kafka-based ingestion

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)

Pay & benefits

• Base pay range: $184,700 - $324,800 per year
• 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 through the Employee Stock Purchase Plan (if voluntarily participating)
• Additionally, this role might 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