Operations Data Engineer

Apple is looking for an Operations Data Engineer to help build modern, scalable data infrastructure that supports analytics, machine learning, and AI-driven decision-making across Operations. This role spans the full lifecycle of data engineering, from end-to-end pipeline design and data modeling to orchestration, observability, and production AIML enablement.

What you’ll do

• Work with business and analytics teams to understand data needs and translate requirements into robust, scalable engineering solutions that support Operations decisions.
• Design and implement end-to-end data pipelines and architectures for both batch and real-time streaming workloads, covering ingestion through transformation to delivery.
• Build and maintain high-quality data models, including dimensional, relational, or knowledge graph-based approaches, using transformation frameworks such as dbt to power analytics and AIML use cases.
• Architect and operate data workflows using orchestration tooling such as Apache Airflow, with monitoring, alerting, and SLA management.
• Implement data observability, lineage tracking, and validation frameworks to ensure data integrity and trustworthiness across the platform.
• Collaborate with Data Scientists, ML Engineers, Software Engineers, and Analysts to operationalize models and ensure infrastructure supports production AIML workflows.
• Partner with infrastructure and platform teams to manage cloud-native data environments using Snowflake, Spark, Delta Lake, and Apache Iceberg, with emphasis on performance, cost efficiency, and scalability.
• Use AI-assisted development tools such as GitHub and Claude, along with LLM-powered agents, to accelerate pipeline authoring, code review, documentation, and transformation logic generation from natural language specifications.
• Apply DataOps practices with CI/CD pipelines, version control, automated testing, and containerization using Docker and Kubernetes to deliver reliable, production-grade data products.
• Support a data product mindset to enable self-serve analytics and reduce bottlenecks for downstream consumers.
• Tune query performance, partitioning strategies, and storage optimization across cloud warehouses and lakehouses.
• Maintain clear technical documentation, including data dictionaries, lineage diagrams, and architecture decision records.
• Communicate 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 expand team capabilities and drive innovation.

Requirements

• MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research, or a related field plus 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 & forecasting, production integration, and channel management.
• Demonstrated experience building and operating large-scale ETL/ELT pipelines using Python, SQL, and modern frameworks such as dbt, Spark, and Kafka/Flink for streaming.
• Proficiency with cloud data platforms (e.g., Snowflake) and open table formats such as Delta Lake and Apache Iceberg.
• Strong ability in advanced SQL for complex data modeling, query optimization, and analytics engineering.
• Experience with workflow orchestration tools such as Apache Airflow (or equivalent) and building production-grade, monitored pipelines.
• Hands-on implementation of 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 (e.g., Tableau, Streamlit, ThoughtSpot) and building light front-end data products.
• Track record of staying current with industry best practices, rapidly adopting 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

Location: Austin, TX (onsite). Minimum experience: 8 years.

Preferred qualifications

• Ability to operate in a fast-paced, iterative environment and deliver work under timeline pressure.
• Experience driving experimentation and continuous learning across reporting, business analytics, and AI-powered automation.
• Exceptional ability to explain complex data architecture decisions clearly to technical peers and non-technical senior stakeholders.
• Strong collaboration skills to partner across functions and integrate diverse feedback.
• Self-sufficient approach with the ability to thrive amid autonomy and ambiguity, with a bias for action and meticulous attention to data integrity.