21 days ago
Responsibilities
- Design, build, and operate massively scalable ETL/ELT pipelines for structured, image, and log data.
- Create real-time data pipelines that produce a trusted, searchable foundation for downstream applications.
- Develop dimensional data models and analytics-optimized schemas.
- Build and maintain distributed processing, streaming, workflow orchestration, and data lake infrastructure.
- Integrate ML model inference, LLMs, and embedding models into data enrichment and transformation pipelines.
- Deploy, serve, and optimize LLMs and ML models in production inference runtimes and serving frameworks.
- Tune batching, KV-cache, and GPU utilization for low-latency, high-throughput real-time inference.
- Apply data governance, security, and privacy best practices.
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 analytics/reporting schema design.
- Experience with SQL and NoSQL databases, including Postgres, Cassandra, and Redis.
- Strong experience with Apache Spark and parallel processing frameworks such as BigTable and Hadoop.
- Strong software engineering fundamentals and experience with Scala and Java.
- Hands-on experience with Apache Kafka, Iceberg, Flink, Apache Airflow, and Beam.
- Experience with AWS services such as S3, EMR, Lambda, Glue, Redshift, BigQuery, and Kinesis.
- Experience with analytics frameworks including Trino, Presto, BigQuery, and Snowflake.
- Hands-on experience with big data lake architectures, Docker, Kubernetes/EKS, and Jenkins.
- Experience with Python and PySpark.
- Familiarity with graph databases such as TigerGraph.
- Experience processing multimodal data and integrating LLM and embedding-model inference.
- Experience deploying, serving, and optimizing production LLMs or ML models using ONNX Runtime, TensorRT/TensorRT-LLM, Triton, vLLM, TorchServe, or similar tools.
- Knowledge of data governance, data security, and data privacy regulations.
- Preferred experience with data versioning tools such as DVC and Delta Lake.
- Preferred experience storing and serving embeddings with pgvector, Milvus, or FAISS.
- Excellent communication skills and a collaborative mindset.
Tech Stack
Amazon RedshiftApache AirflowApache BeamApache CassandraApache FlinkApache HadoopApache KafkaApache SparkAWSDockerDVCGoogle BigQueryJavaJenkinsKubernetesPostgreSQLPrestoPythonRedisScalaSnowflake
Categories
Data EngineeringML Engineering
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.