Data Versioning Engineer Jobs

1 job found

Today

Sr. Data Engineer - Services Special Projects

Apple Cupertino, CA

Cupertino, CA

Today

$185k - $325k/yr Full time Senior Big Data Data Architecture

Apply Now

Job Description

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.

View full job page →

Get Data Versioning Engineer Jobs alerts

New Data Versioning Engineer Jobs, daily.