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# Data Governance Engineer

- **Company:** Apple Inc.
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Information Engineering, Data Governance, Data Infrastructure, DevOps, Distributed Systems, Github, Apache Hadoop, Hadoop Distributed File System, Monitoring of Systems, Apache Hive, Python (Programming Language), Machine Learning, Performance Tuning, Queueing Systems, RabbitMQ, Prometheus, Software Engineering, Rust (Programming Language), Datadog, Data Logging, Data Processing, Delivery Pipeline, Large Language Models, Snowflake, Grafana, Multi-Agent Systems, Prompt Engineering, Apache Spark, Git, Cloudformation, Containerization, Gitlab-ci, Kubernetes, Information Technology, Apache Flink, Apache Kafka, Graphql, Data Management, Machine Learning Operations, Api Design, Terraform, Software Version Control, Data Pipelines, Dynatrace, Docker, Jenkins, Databricks
- **Published:** September 25, 2026
- **Apply:** https://www.dice.com/job-detail/0a9ea359-e98f-4338-9b1d-d83b2ed20165

## About the Role

B.S. in Computer Science or related field with 5+ years of software development experience, including exposure to ML/AI applications

Production experience in Python, Java, Rust or similar languages, with familiarity in data processing and API development

Understanding of distributed systems and cloud platforms, including CAP theorem tradeoffs and basic ML model deployment

Experience with containerization (Docker), orchestration (Kubernetes), and infrastructure as code (e.g. Terraform, CloudFormation)

Proficiency in CI/CD pipelines and DevOps practices using Git, GitHub Actions/Jenkins/GitLab CI, with experience in automated testing and deployment workflows

Familiarity with observability and monitoring tools (e.g. Prometheus, Grafana, DataDog) and logging frameworks for production systems

Basic knowledge of machine learning concepts and MLOps, including data pipelines, model versioning, and experiment tracking tools

Preferred Qualifications

Expertise in open source data analytics and governance platforms: architecture, deployment, and performance tuning of Datahub, Apache Spark, Flink, Hive, Hadoop/HDFS, and Iceberg Rest Catalog

Experience building multi-agent AI systems: proficiency with LangChain, LangGraph, or AutoGen frameworks; strong prompt engineering and LLM integration skills; ability to design event-driven architectures for autonomous workflows

Skills in integration and communication layers: implement MCP servers and APIs using Python, REST/GraphQL, and message queuing (e.g. Kafka, RabbitMQ); experience with modern data platforms including Snowflake, Databricks, and vector databases

MLOps and observability capabilities: deploy containerized AI systems with comprehensive monitoring; track experiments using MLflow or Weights & Biases; implement distributed tracing for agent workflows and model performance

## Description

The Apple Ads Data Governance team develops privacy-centric advertising solutions that leverage advanced data engineering and machine learning technologies at massive scale. The Data Governance Engineer role involves collaborating with cross-functional teams to implement critical privacy safeguards and data management controls., At Apple Ads, we are building the next generation of privacy-focused advertising capabilities. In the Data Governance team, we work at the cutting edge of data engineering, machine learning, and privacy at Apple's scale. We are constantly developing data and privacy management products to provide amazing user experiences and to drive value for developers and partners.


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