Operations Data Engineer

An Operations Data Engineer at Apple will help design and build modern, scalable data infrastructure that supports analytics, machine learning, and AI-driven decision-making across Operations. The role spans end-to-end pipelines, data modeling, orchestration and observability, and production AIML workflows.

Responsibilities

• Partner with business and analytics teams to understand data needs and translate requirements into robust, scalable engineering solutions that influence Operations decisions
• Design and implement end-to-end data pipelines and architectures across ingestion, transformation, and delivery for batch and real-time streaming workloads
• Develop and maintain high-quality data models, including dimensional, relational, and knowledge graph-based approaches, using modern transformation frameworks such as dbt
• Architect and run data workflows using orchestration tools such as Apache Airflow, including monitoring, alerting, and SLA management
• Implement data observability, lineage tracking, and validation frameworks to maintain data integrity and trust across the platform
• Collaborate with Data Scientists, ML Engineers, Software Engineers, and Analysts to operationalize models and ensure infrastructure supports production AIML workflows
• Work with infrastructure and platform teams to manage cloud-native data environments using Snowflake, Spark, and open table formats such as Delta Lake and Apache Iceberg, with emphasis on performance, cost efficiency, and scalability
• Use AI-assisted development tooling (including GitHub and Claude) and LLM-powered agents to accelerate pipeline authoring, code review, documentation, and transformation logic generation from natural language specifications
• Apply DataOps practices including CI/CD, version control, automated testing, and containerization with Docker and Kubernetes to deliver reliable, production-grade data products
• Adopt a data product mindset that enables self-serve analytics and reduces bottlenecks for downstream consumers
• Tune query performance, partitioning strategies, and storage optimization for data at scale in cloud warehouses and lakehouses
• Create and maintain technical documentation, including data dictionaries, lineage diagrams, and architecture decision records
• Explain data infrastructure capabilities, health metrics, and architectural recommendations to senior leadership in clear, non-technical terms
• Research and evaluate emerging data engineering technologies, including streaming architectures, GenAI-powered data tooling, and next-generation warehousing to improve team capabilities

Requirements

• MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research, or related field and 8+ years of industry experience, or BS in a related field with 10+ years of hands-on industry experience
• Domain expertise in supply chain, operations management, logistics, planning and forecasting, production integration, and channel management
• Proven experience building and operating large-scale ETL/ELT pipelines with Python, SQL, and modern frameworks including dbt, Spark, and Kafka/Flink
• Proficiency with cloud data platforms (such as Snowflake) and open table formats including Delta Lake and Apache Iceberg
• Strong advanced SQL skills for complex data modeling, query optimization, and analytics engineering
• Experience with workflow orchestration tools such as Apache Airflow (or equivalent) and delivering production-grade monitored pipelines
• Hands-on implementation experience for data quality frameworks, observability tooling, and data lineage tracking in production environments
• Experience implementing and productionalizing GenAI and Agentic AI tooling, including LLM-assisted code generation, MCP servers, and AI-powered data pipeline automation
• Experience with data visualization and self-service analytics platforms (such as Tableau, Streamlit, ThoughtSpot) and ability to build lightweight front-end data products
• Track record of adopting industry best practices, integrating emerging technologies (including vector databases, RAG pipelines, and AI-native data tools), and building functional prototypes to validate concepts

Technologies

• Python, SQL, dbt, Spark, Kafka, Flink, Snowflake
• Delta Lake, Apache Iceberg, Apache Airflow
• GitHub, Claude, LLM-powered agents
• Docker, Kubernetes
• Tableau, Streamlit, ThoughtSpot
• MCP servers, vector databases, RAG pipelines

Preferred Qualifications

• Ability to work in a fast-paced, iterative environment and deliver projects under timeline pressure
• Strong bias toward experimentation and continuous learning, with innovative thinking for reporting, business analytics, and AI-powered automation
• Exceptional ability to communicate complex data architecture decisions to both technical peers and non-technical senior stakeholders
• Strong interpersonal and collaboration skills for cross-functional partnership, knowledge sharing, and incorporating diverse feedback
• Self-sufficient working style in environments with autonomy and ambiguity, with a bias for action and careful attention to data integrity