Operations Data Engineer

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Austin, TX
Posted
148 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $169k
$117k most similar roles pay here $221k

This listing doesn't post a salary. Most similar roles pay $126,800–$211,200.

Based on 240 similar postings.

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About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 1984 open roles on FindRole.

Listed pay typically runs $175,000–$277,600 across 1590 roles with salary data.

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At a glance

TL;DR · Operations Data Engineer

As an Operations Data Engineer, you will join the team designing and building modern, scalable data infrastructure to power analytics, machine learning, and AI-driven decision-making across complex supply chain operations. You will be responsible for designing end-to-end data pipelines, creating high-quality dimensional or relational data models using dbt, and managing workflows with Apache Airflow. The role involves implementing data observability, lineage tracking, and DataOps principles like CI/CD and containerization using Docker and Kubernetes. You will work with Snowflake, Spark, Delta Lake, and Apache Iceberg while utilizing Python, SQL, and Kafka or Flink for streaming. Additionally, you will leverage AI-assisted development tools and LLM-powered agents to automate pipeline logic. Your work addresses the challenge of managing supply chain complexity through robust data products that support production AIML workflows and self-serve analytics.

What you'll do

  • Translate business and analytics requirements into robust, scalable engineering solutions for operations decisions.
  • Design and implement end-to-end data pipelines for both batch and real-time streaming workloads.
  • Build and maintain high-quality data models using modern transformation frameworks like dbt to power AI/ML use cases.
  • Architect and operate orchestrated data workflows with integrated monitoring, alerting, and SLA management.
  • Implement data observability, lineage tracking, and validation frameworks to ensure data integrity.
  • Manage cloud-native data environments including Snowflake, Spark, and Delta Lake for performance and cost efficiency.
  • Apply DataOps principles such as CI/CD, version control, and containerization to deliver production-grade products.
  • Utilize AI-assisted development tools and LLM-powered agents to accelerate pipeline authoring and code generation.

What we're looking for

  • Hold an MS in a related field with 8+ years of experience or a BS in a related field with 10+ years of experience.
  • Possess domain expertise in supply chain, operations management, logistics, planning, forecasting, and production integration.
  • Demonstrate expertise building large-scale ETL/ELT pipelines using Python, SQL, dbt, Spark, and Kafka or Flink.
  • Exhibit proficiency with cloud data platforms like Snowflake and open table formats such as Delta Lake or Apache Iceberg.
  • Maintain a strong command of advanced SQL for complex data modeling, query optimization, and analytics engineering.
  • Experience with workflow orchestration tools like Apache Airflow and implementing data quality, observability, and lineage tracking.
  • Experience with GenAI and Agentic AI tooling, including LLM-assisted code generation and AI-powered pipeline automation.
  • Ability to develop data visualization products using platforms such as Tableau, Streamlit, or ThoughtSpot.

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