Operations Data Engineer Austin, Texas, United States Software and Services Summary Posted:

Apr 15, 2026

Weekly Hours:

40

Role Number:

200655798-0157 Apple is where extraordinary people do their best work. If making a real impact excites you, a career here might be your dream — just be prepared to dream big. Apple's growing supply chain complexity demands innovative approaches beyond traditional data engineering. You'll join a team designing and building modern, scalable data infrastructure that powers analytics, machine learning, and AI-driven decision-making across Operations. You're passionate about building reliable data systems, staying ahead of technology trends, and thrive navigating ambiguity in a fast-paced environment. If this sounds like you, we'd love to talk. Description Engage with business and analytics teams to deeply understand data needs and translate requirements into robust, scalable engineering solutions that directly impact Operations decisionsDesign and implement end-to-end data pipelines and architectures from ingestion and transformation to delivery across batch and real-time streaming workloadsBuild and maintain high-quality data models (dimensional, relational, or knowledge graph-based) using modern transformation frameworks such as dbt, powering analytics and AIML use cases at scaleArchitect and operate data workflows using orchestration tools (e.g., Apache Airflow, etc) with built-in monitoring, alerting, and SLA managementImplement data observability, lineage tracking, and validation frameworks to uphold data integrity and trustworthiness across the platformCollaborate with Data Scientists, ML Engineers, Software Engineers and Analysts to operationalize models and ensure data infrastructure supports production AIML workflowsPartner with infrastructure and platform teams to manage cloud-native data environments (Snowflake, Spark, Delta Lake / Apache Iceberg) with a focus on performance, cost efficiency, and scalabilityLeverage AI-assisted development tools (e.g., GitHub, Claude) and LLM-powered agents to accelerate pipeline authoring, code review, documentation, and transformation logic generation from natural language specifications DataOps principles including CI/CD pipelines, version control, automated testing, and containerization (Docker, Kubernetes) to deliver reliable, production-grade data productsChampion a data product mindset, enabling self-serve analytics and reducing bottlenecks for downstream consumersTune query performance, partitioning strategies, and storage optimization for data at scale in cloud warehouses and lakehousesDevelop and maintain clear technical documentation including data dictionaries, lineage diagrams, and architecture decision recordsPresent data infrastructure capabilities, health metrics, and architectural recommendations to senior leadership in clear, non-technical termsResearch and evaluate emerging data engineering technologies including streaming architectures, GenAI-powered data tooling, and next-generation warehousing to expand the team's capabilities and accelerate innovation Minimum Qualifications MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research or a related field and 8+ years of industry experience OR BS in related field with 10+ years hands-on industry experience Domain expertise in supply chain, operations management, logistics, planning & forecasting, production integration, channel management Demonstrated expertise building and operating large-scale ETL/ELT pipelines using Python, SQL, and modern frameworks (dbt, Spark, Kafka/Flink for streaming) Proficiency with cloud data platforms (e.g. Snowflake) and open table formats (Delta Lake, Apache Iceberg) Strong command of advanced SQL for complex data modeling, query optimization, and analytics engineering Experience with workflow orchestration tools (Apache Airflow or equivalent) and building production-grade, monitored pipelines Hands-on experience implementing data quality frameworks, observability tooling, and data lineage tracking in production environments Experienced with implementation and productionalization of 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 the ability to build light front-end data products Track record of staying current with industry best practices, rapidly adopting emerging technologies (e.g., vector databases, RAG pipelines, AI-native data tools), and building functional prototypes to validate concepts Preferred Qualifications Ability to work well in a fast-paced, iterative environment and deliver projects under timeline pressures Champion a culture of experimentation and continuous learning, bringing innovative and strategic thinking to reporting, business analytics, and AI-powered automation Exceptional ability to communicate complex data architecture decisions clearly to both technical peers and non-technical senior stakeholders Strong interpersonal and collaboration skills to partner effectively across functions, share knowledge, and integrate diverse feedback Self-sufficient with an ability to thrive in an environment of autonomy amidst ambiguity, with a high bias for action and meticulous attention to data integrity Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. At Apple, we believe accessibility is a fundamental human right. You'll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong. Apple accepts applications to this posting on an ongoing basis.