Data Orchestration Jobs

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Operations Data Engineer

Apple Austin, TX

Austin, TX

Today

Full time Ai/ml Data Engineering Artificial Intelligence Data

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Job Description

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.

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